Data processing apparatus and related products

ABSTRACT

The present disclosure provides a data processing apparatus and related products. The products include a control module including an instruction caching unit, an instruction processing unit, and a storage queue unit. The instruction caching unit is configured to store computation instructions associated with an artificial neural network operation; the instruction processing unit is configured to parse the computation instructions to obtain a plurality of operation instructions; and the storage queue unit is configured to store an instruction queue, where the instruction queue includes a plurality of operation instructions or computation instructions to be executed in the sequence of the queue. By adopting the above-mentioned method, the present disclosure can improve the operation efficiency of related products when performing operations of a neural network model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a bypass continuation application of and claims the benefit ofpriority of PCT Application No. PCT/CN2020/082803 filed Apr. 1, 2020,which further claims benefit of priority to Chinese Application No.201910272454.7 filed Apr. 4, 2019, Chinese Application No.201910272513.0 filed Apr. 4, 2019, Chinese Application No.201910319175.1 filed Apr. 19, 2019, Chinese Application No.201910694672.X filed Jul. 30, 2019, Chinese Application No.201910735425.X filed Aug. 9, 2019, Chinese Application No.201910735424.5 filed Aug. 9, 2019, Chinese Application No.201910735393.3 filed Aug. 9, 2019, and Chinese Application No.201910734749.1 filed Aug. 9, 2019. The contents of all theseapplications are incorporated herein by reference in their entireties.

TECHNICAL FIELD

The disclosure relates to the field of computer technologies, andparticularly to a data processing apparatus and related products.

BACKGROUND

With the continuous development of AI (Artificial Intelligence)technology, the amount of data and data dimensions that need to beprocessed are increasing. In related arts, processors usually determinethe data address by obtaining parameters of instructions, and then readand use data according to the data address. Therefore, those skilled inthe art are required to set relevant parameters (such as therelationship between data or between data dimensions, etc.) for dataaccess when designing parameters, so as to generate instructions andsend the instructions to processors to complete the data access. Theabove-mentioned method reduces the processing efficiency of theprocessors.

SUMMARY

In view of the above, the present disclosure provides a data processingtechnical solution.

A first aspect of the present disclosure provides a data processingapparatus including a control circuit and an execution circuit, wherethe control circuit includes a tensor control module, and the controlcircuit may be configured to:

when an operand of a decoded first processing instruction includes anidentifier of a descriptor, determine a descriptor storage spacecorresponding to the descriptor by the tensor control module accordingto the identifier of the descriptor, wherein the descriptor indicates ashape of a tensor data;

obtain content of the descriptor from the descriptor storage space; and

send the content of the descriptor and the first processing instructionto the execution circuit, for the execution circuit to execute the firstprocessing instruction according to the content of the descriptor.

The execution circuit is configured to execute the first processinginstruction on the tensor data obtained from the data address.

A second aspect of the present disclosure provides an artificialintelligence chip including the data processing apparatus.

A third aspect of the present disclosure provides an electronic deviceincluding the artificial intelligence chip.

A fourth aspect of the present disclosure provides a board cardincluding: a storage device, an interface apparatus, a control device,and the above-mentioned artificial intelligence chip. The artificialintelligence chip is connected to the storage device, the controldevice, and the interface apparatus respectively; the storage device isconfigured to store data; the interface apparatus is configured toimplement data transfer between the artificial intelligence chip and anexternal equipment; and the control device is configured to monitor astate of the artificial intelligence chip.

A fifth aspect of the present disclosure provides a data processingmethod performed by an artificial intelligence chip, including:

-   -   determining, by a control circuit, that an operand of a first        processing instruction includes an identifier of a descriptor,        wherein content of the descriptor indicates a shape of tensor        data on which the first processing instruction is to be        executed;    -   determining, by the control circuit, a descriptor storage space        corresponding to the descriptor according to the identifier of        the descriptor;    -   obtaining, by the control circuit, the content of the descriptor        from the descriptor storage space;    -   determining, by the control circuit, a data address of the        tensor data to be used as the operand of the first processing        instruction in the data storage space according to the content        of the descriptor; and    -   executing, by an execution circuit, the first processing        instruction on the tensor data obtained from the data address.

According to the data processing apparatus provided in the presentdisclosure, by introducing the descriptor indicating the shape of thetensor and configuring the tensor control module in the control circuit,the corresponding descriptor storage space may be determined by thetensor control module when the operand of the decoded processinginstruction includes the identifier of the descriptor, the content ofthe descriptor may be obtained from the descriptor storage space, andthe content of the descriptor and the processing instruction may be sentto the execution unit, so that the execution unit executes theinstruction according to the content of the descriptor. Accordingly, thecomplexity of data access can be reduced and the efficiency of dataaccess improved.

In order to make other features and aspects of the present disclosureclearer, a detailed description of exemplary embodiments with referenceto the drawings is provided below.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings contained in and forming part of thespecification together with the specification show exemplaryembodiments, features and aspects of the present disclosure and are usedto explain the principles of the disclosure.

FIG. 1a shows a block diagram of a data processing apparatus accordingto an embodiment of the present disclosure.

FIG. 1b 1 shows a block diagram of a data processing apparatus accordingto an embodiment of the present disclosure.

FIG. 1b 2 shows a flowchart of a data processing method according to anembodiment of the present disclosure.

FIG. 1c shows a flowchart of a data processing method according to anembodiment of the present disclosure.

FIG. 1d 1 shows a schematic diagram of a processing system of a datasynchronization method according to an embodiment of the presentdisclosure.

FIG. 1d 2 shows a flowchart of a data synchronization method accordingto an embodiment of the present disclosure.

FIG. 1e shows a flowchart of a data synchronization method according toan embodiment of the present disclosure.

FIG. 1f shows a flowchart of a data synchronization method according toan embodiment of the present disclosure.

FIG. 1g shows a flowchart of a data synchronization method according toan embodiment of the present disclosure.

FIG. 1h shows a flowchart of a data synchronization method according toan embodiment of the present disclosure.

FIG. 2 shows a schematic diagram of a data storage space according to anembodiment of the present disclosure.

FIG. 3a shows a block diagram of a data processing apparatus accordingto an embodiment of the present disclosure.

FIG. 3b 1 shows a flowchart of a data synchronization method accordingto an embodiment of the present disclosure.

FIG. 3b 2 shows a flowchart of a data synchronization method accordingto an embodiment of the present disclosure.

FIG. 3b 3 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure.

FIG. 3b 4 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure.

FIG. 3b 5 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure.

FIG. 3d shows a flowchart of a data synchronization method according toan embodiment of the present disclosure.

FIG. 3c 2 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure.

FIG. 3c 3 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure.

FIG. 3d 1 shows a flowchart of a data synchronization method accordingto an embodiment of the present disclosure.

FIG. 3d 2 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure.

FIG. 3d 3 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure.

FIG. 3e 1 shows a flowchart of a data synchronization method accordingto an embodiment of the present disclosure.

FIG. 3e 2 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure.

FIG. 3e 3 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure.

FIG. 3f 1 shows a flowchart of a data synchronization method accordingto an embodiment of the present disclosure.

FIG. 3f 2 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure.

FIG. 3f 3 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure.

FIG. 4 shows a block diagram of a board card according to an embodimentof the present disclosure.

DETAILED DESCRIPTIONS

Various exemplary embodiments, features, and aspects of the presentdisclosure will be described in detail below with reference to thedrawings. The same labels in the drawings represent the same or similarelements. Although various aspects of the embodiments are shown in thedrawings, the drawings are not necessarily drawn to scale unlessspecifically noted.

The “exemplary” as used herein means “serving as an example, embodiment,or illustration.” Any embodiment described herein as “exemplary” is notnecessarily to be interpreted as superior to or better than otherembodiments.

In addition, various specific details are provided for betterillustration and description of the present disclosure. Those skilled inthe art should understand that the present disclosure can be implementedwithout certain specific details. In some embodiments, methods, means,components, and circuits that are well known to those skilled in the arthave not been described in detail in order to highlight the main idea ofthe present disclosure.

One aspect of the present disclosure provides a data processingapparatus. FIG. 1a shows a block diagram of a data processing apparatusaccording to an embodiment of the present disclosure. As shown in FIG.1a , the data processing apparatus includes a control circuit 11 a andan execution circuit 12 a, where the control circuit 11 a includes atensor control module 111 a, and the control circuit 11 a may beconfigured to:

when an operand of a decoded first processing instruction includes anidentifier of a descriptor, determine a descriptor storage spacecorresponding to the descriptor by the tensor control module accordingto the identifier of the descriptor, wherein the descriptor indicates ashape of a tensor data;

obtain content of the descriptor from the descriptor storage space; and

send the content of the descriptor and the first processing instructionto the execution circuit, for the execution circuit to execute the firstprocessing instruction according to the content of the descriptor.

According to the data processing apparatus provided in the presentdisclosure, by introducing the descriptor indicating the shape of thetensor and setting the tensor control module in the control circuit, thecorresponding descriptor storage space may be determined by the tensorcontrol module when the operand of the decoded processing instructionincludes the identifier of the descriptor, the content of the descriptormay be obtained from the descriptor storage space, and the content ofthe descriptor and the processing instruction may be sent to theexecution circuit, so that the execution circuit executes theinstruction according to the content of the descriptor, which can reducethe complexity of data access and improve the efficiency of data access.

For example, the data processing apparatus may be a processor, where theprocessor may include a general-purpose processor (such as a CPU(central processing unit), a GPU (graphics processor)) and a dedicatedprocessor (such as an AI processor, a scientific computing processor, ora digital signal processor, etc.). This disclosure does not limit thetype of the processor.

In some embodiments, the data processing apparatus includes the controlcircuit 11 a and the execution circuit 12 a, where the control circuit11 a is configured to control devices, for example, the control circuit11 a may read an instruction of a memory or an externally inputinstruction, decode the instruction, and send a micro-operation controlsignal to corresponding components. The execution circuit 12 a isconfigured to execute specific instructions, where the execution circuit12 a may be, for example, an ALU (arithmetic and logic unit), an MAU(memory access unit), an NFU (neural functional unit), etc. The presentdisclosure does not limit the specific hardware type of the executioncircuit 12 a.

In some embodiments, data processed by the data processing apparatus mayinclude N-dimensional tensor data (N is an integer greater than or equalto 0, for example, N=1, 2, or 3). Conventionally, a processinginstruction usually includes one or more operands and each operandincludes the data address of data on which the processing instruction isto be executed. The data can be tensor data or scalar data. However, thedata address only indicates the storage area in a memory of a dataprocessing apparatus where the tensor data is stored. It neitherindicates the shape of the tensor data, nor identifies the relatedinformation such as the relationship between this tensor data and othertensor data and the relationship between this data dimension and otherdata dimensions. As a result, the processor is inefficient in accessingtensor data.

When tensor data is stored in a memory of a data processing apparatus,the shape of the tensor data cannot be determined according to a dataaddress (or storage area) of the tensor, and then related informationsuch as the relationship between tensor data or between data dimensionscannot be determined. As a result, the processor is inefficient inaccessing tensor data.

In view of this, a descriptor (tensor descriptor) is introduced toindicate the shape of the tensor (N-dimensional tensor data), where thevalue of N can be determined according to a count of dimensions (orders)of the tensor data, and can also be set according to the usage of thetensor data. For example, when the value of N is 3, the tensor data is3-dimensional tensor data, and the descriptor can be used to indicatethe shape (such as offset, size, etc.) of the 3-dimensional tensor datain three dimensions. It should be understood that those skilled in theart can set the value of N according to actual needs, which is notlimited in the present disclosure.

In some embodiments, the descriptor may include an identifier andcontent. The identifier of the descriptor may be used to distinguishdescriptors. For example, the identifier may be a serial number. Thecontent of the descriptor may include at least one shape parameter (suchas a size of each dimension of the tensor, etc.) representing the shapeof the tensor data, and may also include at least one address parameter(such as a base address of a datum point) representing an address of thetensor data. The present disclosure does not limit the specificparameters included in the content of the descriptor.

By using the descriptor to indicate tensor data, information of thetensor data, such as the shape, position, relationship between tensordata, and the relationship between data dimensions, may be representedaccordingly, thus improving the efficiency of accessing tensor data.

In some embodiments, a tensor control module 111 a may be arranged inthe control circuit 11 a to implement operations associated with thedescriptor, where the operations may include registration, modification,and release of the descriptor, reading and writing of the content of thedescriptor, etc. The tensor control module 111 a may be, for example, aTIU (Tensor interface Unit). The present disclosure does not limit thespecific hardware type of the tensor control module. In this way, theoperations associated with the descriptor can be implemented by specialhardware, which further improves the access efficiency of tensor data.

In some embodiments, when the data processing apparatus receives aprocessing instruction, the data processing apparatus decodes theprocessing instruction through the control circuit 11 a. The controlcircuit 11 a is further configured to decode the received firstprocessing instruction to obtain a decoded first processing instruction.The decoded first processing instruction includes an operation code andone or more operands, where the operation code is used to indicate aprocessing type corresponding to the first processing instruction.

In this case, after the first processing instruction is decoded by thecontrol circuit 11 a, a decoded first processing instruction(microinstruction) may be obtained. The first processing instruction mayinclude a data access instruction, an operation instruction, adescriptor management instruction, a synchronization instruction, andthe like. The present disclosure does not limit the specific type of thefirst processing instruction and the specific manner of decoding.

The decoded first processing instruction includes an operation code andone or more operands, where the operation code is used to indicate aprocessing type corresponding to the first processing instruction, andthe operand is used to indicate data to be processed. For example, theinstruction can be represented as: Add; A; B, where Add is an operationcode, A and B are operands, and the instruction is used to add A and B.The present disclosure does not limit a count of operands involved inthe operation and representation of the decoded instruction.

In some embodiments, if the operand of the decoded first processinginstruction includes the identifier of the descriptor, a storage spacein which the descriptor is stored can be determined according to theidentifier of the descriptor; and content (including informationindicating the shape, the address, etc.) of the descriptor can beobtained from the descriptor storage space; and then the control circuit11 a may send the content of the descriptor and the first processinginstruction to the execution circuit, so that the execution circuitexecutes the first processing instruction according to the content ofthe descriptor.

In some embodiments, the execution circuit is configured to:

determine a data address of the data corresponding to the operand of thefirst processing instruction in the data storage space according to thecontent of the received descriptor and the first processing instruction;and

execute data processing corresponding to the first processinginstruction according to the data address.

In other words, when the content of the descriptor and the firstprocessing instruction are received by the execution circuit 12 a, theexecution circuit 12 a may compute the data address at which the data ofeach operand is stored in the data storage space according to thecontent of the descriptor. The execution circuit 12 a then obtains thedata from the data addresses and perform a computation on the operanddata according to the first processing instruction.

For example, for the instruction Add; A; B, if operands A and B includeidentifiers TR1 and TR2 of the descriptor, respectively, the controlcircuit may determine the descriptor storage spaces corresponding to TR1and TR2 respectively, and the control circuit may read the content (suchas a shape parameter and an address parameter) of the descriptor storagespaces and send the content to the execution circuit. After receivingthe content of the descriptor, the execution circuit may compute dataaddresses of data A and B. For example, a data address 1 of A in amemory is ADDR64-ADDR127, and a data address 2 of B in the memory isADDR1023-ADDR1087. Then, the execution circuit can read data A and Bfrom the address 1 and the address 2 respectively, execute an addition(Add) operation on A and B, and obtain an operation result (A+B).

By adopting the above-mentioned method provided by the presentdisclosure, the content of the descriptor can be obtained from thedescriptor storage space, and then the data address can be obtained. Inthis way, it is not necessary to input the address through aninstruction during each data access, thus improving the data accessefficiency of the processor.

In some embodiments, the identifier and content of the descriptor can bestored in the descriptor storage space, where the descriptor storagespace can be a storage space in an internal memory (such as a register,an on-chip SRAM, or other medium cache, etc.) of the control circuit.Similarly, the data storage space of the tensor data indicated by thedescriptor may also be a storage space in the internal memory (such asan on-chip cache) of the control circuit or a storage space in anexternal memory (an off-chip memory) connected to the control circuit.The data address of the data storage space may be an actual physicaladdress or a virtual address. The present disclosure does not limit aposition of the descriptor storage space and a position of the datastorage space, and the type of the data address.

In some embodiments, the identifier and content of the descriptor, andtensor data indicated by the descriptor can be stored in a same area.For example, a continuous area of an on-chip cache with addressesADDR0-ADDR1023 can be used to store the above information. Within thisarea, addresses ADDR0-ADDR31 can be used to store the identifier of thedescriptor, addresses ADDR32-ADDR63 can be used to store the content ofthe descriptor, and addresses ADDR64-ADDR1023 can be used to store thetensor data indicated by the descriptor. The address ADDR is not limitedto 1 bit or 1 byte, and is an address unit used to represent an address.Those skilled in the art can determine the storage area and the addressthereof according to the specific applications, which is not limited inthe present disclosure.

In some embodiments, the identifier and content of the descriptor, andthe tensor data indicated by the descriptor can be respectively storedin different areas of an internal memory. For example, a register can beused as a descriptor storage space to store the identifier and contentof the descriptor, and an on-chip cache can be used as a data storagespace to store the tensor data indicated by the descriptor.

In some embodiments, a special register (SR) may be provided for thedescriptor, where data in the descriptor may be an immediate number orbe obtained from the special register. When the register is used tostore the identifier and content of the descriptor, a serial number ofthe register can be used to indicate the identifier of the descriptor.For example, if the serial number of the register is 0, the identifierof a descriptor stored in the register is 0. When the descriptor in theregister is valid, an area can be allocated in a caching space (such ascreating a tensor caching unit for each piece of tensor data in thecache) according to a size of tensor data indicated by the descriptorfor storing the tensor data. It should be understood that a presetcaching space may also be used to store the tensor data, which is notlimited in the present disclosure.

In some embodiments, the identifier and content of the descriptor can bestored in an internal memory, and the tensor data indicated by thedescriptor can be stored in an external memory. For example, theidentifier and content of the descriptor may be stored on-chip and thetensor data indicated by the descriptor may be stored off-chip.

In some embodiments, the data address of the data storage spaceidentified by the descriptor may be a fixed address. For example, aseparate data storage space may be allocated for tensor data, where astart address of each piece of tensor data in the data storage spacecorresponds to one identifier of the descriptor. In this case, theexecution circuit can determine a data address of data corresponding tothe operand according to the content of the descriptor, and then executethe first processing instruction.

In some embodiments, when the data address of the data storage spacecorresponding to the identifier of the descriptor is a variable address,the descriptor may be also used to indicate an address of N-dimensionaltensor data, where the content of the descriptor may further include atleast one address parameter representing the address of the tensor data.For example, if the tensor data is 3-dimensional data, when thedescriptor points to the address of the tensor data, the content of thedescriptor may include an address parameter indicating the address ofthe tensor data, such as a start address of the tensor data; or thecontent of the descriptor may include a plurality of address parametersof the address of the tensor data, such as a start address+addressoffset of the tensor data, or address parameters of the tensor data ineach dimension. Those skilled in the art can set the address parametersaccording to actual needs, which is not limited in the presentdisclosure.

In some embodiments, the address parameter of the tensor data includes abase address of the datum point of the descriptor in the data storagespace of the tensor data, where the base address may vary from differentdatum points. The present disclosure does not limit the selection of thedatum point.

In some embodiments, the base address may include a start address of thedata storage space. When the datum point of the descriptor is a firstdata block of the data storage space, the base address of the descriptoris the start address of the data storage space. When the datum point ofthe descriptor is other data than the first data block in the datastorage space, the base address of the descriptor is the physicaladdress of the data block in the data storage space.

In some embodiments, the shape parameter of the tensor data includes atleast one of the followings: a size of the tensor data in at least oneof the N dimensions, a size of the storage area in at least one of the Ndimensions, an offset of the storage area in at least one of the Ndimensions, a position of at least two vertices at diagonal positions inthe N dimensions relative to the datum point, and a mapping relationshipbetween a data description position of the tensor data indicated by thedescriptor and the data address of the tensor data indicated by thedescriptor. The data description position is a mapping position of apoint or an area in the tensor data indicated by the descriptor, forexample, if the tensor data is 3-dimensional data, the descriptor canuse a coordinate (x, y, z) to represent the shape of the tensor data,and the data description position of the tensor data can be representedby the coordinate (x, y, z), and the data description position of thetensor data may be a position of a point or an area to which the tensordata is mapped in a 3-dimensional space.

It should be understood that those skilled in the art may select a shapeparameter representing tensor data according to actual conditions, whichis not limited in the present disclosure.

FIG. 2 shows a schematic diagram of a data storage space of a dataprocessing apparatus according to an embodiment of the presentdisclosure. As shown in FIG. 2, a data storage space 21 stores a pieceof 2-dimensional data in a row-first manner, where the data storagespace 21 can be represented by (x, y) (where the X axis extendshorizontally to the right, and the Y axis extends vertically down), asize in the X axis direction (a size of each row) is ori_x (which is notshown in the figure), a size in the Y axis direction (a total count ofrows) is ori_y (which is not shown in the figure), and a start addressPA_start (a base address) of the data storage space 21 is a physicaladdress of a first data block 22. A data block 23 is part of the data inthe data storage space 21, where an offset 25 of the data block 23 inthe X axis direction is represented as offset_x, an offset 24 of thedata block 23 in the Y axis direction is represented as offset_y, thesize in the X axis direction is denoted by size_x, and the size in the Yaxis direction is denoted by size_y.

In some embodiments, when the descriptor is used to define the datablock 23, the datum point of the descriptor may be a first data block ofthe data storage space 21, the base address of the descriptor is thestart address PA_start of the data storage space 21, and then thecontent of the descriptor of the data block 23 may be determinedaccording to the size ori_x of the data storage space 21 in the X axis,the size ori_y of the data storage space 21 in the Y axis, the offsetoffset_y of the data block 23 in the Y axis direction, the offsetoffset_x of the data block 23 in the X axis direction, the size size_xof the data block 23 in the X axis direction, and the size size_y of thedata block 23 in the Y axis direction.

In some embodiments, the content of the descriptor may be structured asshown by the following formula:

$\{ \begin{matrix}{{X{direction}:{ori\_ x}},{offset\_ x},{size\_ x}} \\{{Y{direction}:{ori\_ y}},{offset\_ y},{size\_ y}} \\{PA\_ start}\end{matrix} $

It should be understood that although the descriptor describes a2-dimensional space in the above-mentioned example, those skilled in theart can set the dimensions represented by the content of the descriptoraccording to actual situations, which is not limited in the presentdisclosure.

In some embodiments, the content of the descriptor of the tensor datamay be determined according to the base address of the datum point ofthe descriptor in the data storage space and the position of at leasttwo vertices at diagonal positions in N dimensions relative to the datumpoint.

For example, the content of the descriptor of the data block 23 in FIG.2 can be determined according to the base address PA_base of the datumpoint of the descriptor in the data storage space and the position oftwo vertices at diagonal positions relative to the datum point. First,the datum point of the descriptor and the base address PA_base in thedata storage space are determined. For example, a piece of data (such asa piece of data at position (2, 2)) in the data storage space 21 isselected as a datum point, and a physical address of the selected datain the data storage space is used as the base address PA_base. Then, thepositions of at least two vertices at diagonal positions of the datablock 23 relative to the datum point are determined. For example, thepositions of vertices at diagonal positions from the top left to thebottom right relative to the datum point are used, where the relativeposition of the top left vertex is (x_min, y_min), and the relativeposition of the bottom right vertex is (x_max, y_max). And then thecontent of the descriptor of the data block 23 can be determinedaccording to the base address PA_base, the relative position (x_min,y_min) of the top left vertex, and the relative position (x_max, y_max)of the bottom right vertex.

In some embodiments, the content of the descriptor can be structured asshown by the following formula:

$\{ \begin{matrix}{{X\ {direction}:\ {x\_ min}},{x\_ max}} \\{{Y\ {direction}:\ y_{-}\min},{y\_ max}} \\{PA\_ base}\end{matrix} $

It should be understood that although the top left vertex and the bottomright vertex are used to determine the content of the descriptor in theabove-mentioned example, those skilled in the art may set at least twospecific vertices according to actual needs, which is not limited in thepresent disclosure.

In some embodiments, the content of the descriptor of the tensor datacan be determined according to the base address of the datum point ofthe descriptor in the data storage space and a mapping relationshipbetween the data description position of the tensor data indicated bythe descriptor and the data address of the tensor data indicated by thedescriptor. The mapping relationship between the data descriptionposition and the data address can be set according to actual needs. Forexample, when the tensor data indicated by the descriptor is3-dimensional spatial data, the function f (x, y, z) can be used todefine the mapping relationship between the data description positionand the data address.

In some embodiments, the content of the descriptor can also bestructured as shown by the following formula:

$\{ \begin{matrix}{f( {x,y,z} )} \\{PA\_ base}\end{matrix} $

It should be understood that those skilled in the art can set themapping relationship between the data description position and the dataaddress according to actual situations, which is not limited in thepresent disclosure.

When the content of the descriptor is structured according to theformula

$\begin{matrix}{{X{direction}:{ori\_ x}},{offset\_ x},{size\_ x}} \\{{Y{direction}:{ori\_ y}},{offset\_ y},{size\_ y}} \\{PA\_ start}\end{matrix},$

for any datum point in the tensor data, the data description position isset to (x_q, y_q), and then the data address PA2_((x,y)) of the data inthe data storage space can be determined using the following formula:

PA2_((x,y)) =PA_start+(offset_y+y _(q)−1)*ori_x+(offset_x+x _(q))  (4)

By adopting the above-mentioned method provided by the presentdisclosure, the execution circuit may compute the data address of thetensor data indicated by the descriptor in the data storage spaceaccording to the content of the descriptor, and then execute processingcorresponding to the processing instruction according to the address.

In some embodiments, registration, modification, and release operationsof the descriptor can be performed through management instructions ofthe descriptor, and corresponding operation codes are set for themanagement instructions. For example, a descriptor can be registered(created) through a descriptor registration instruction (TRCreat);various parameters (shape, address, etc.) of the descriptor can bemodified through the descriptor modification instruction; and thedescriptor can be released (deleted) through the descriptor releaseinstruction (TRRelease). The present disclosure does not limit the typesof the management instructions of the descriptor and the operationcodes.

In some embodiments, the control circuit is further configured to:

when the first processing instruction is a descriptor registrationinstruction, obtain a registration parameter of the descriptor in thefirst processing instruction, where the registration parameter includesat least one of the identifier of the descriptor, the shape of thetensor, and the content of the tensor data indicated by the descriptor;

determine a first storage area for the content of the descriptor in thedescriptor storage space, and a second storage area for the tensorindicated by the content of the descriptor in the data storage spacethrough the tensor control module;

determine content of the descriptor according to the registrationparameter of the descriptor and the second storage area to establish acorrespondence between the descriptor and the second storage area; and

store the content of the descriptor into the first storage area.

For example, the descriptor registration instruction may be used toregister a descriptor, and the instruction may include a registrationparameter of the descriptor. The registration parameter may include atleast one of the identifier (ID) of the descriptor, the shape of thetensor, and the tensor data indicated by the descriptor. For example,the registration parameter may include an identifier TR0 and the shapeof the tensor (a count of dimensions, a size of each dimension, anoffset, a start data address, etc.). The present disclosure does notlimit the specific content of the registration parameter.

In some embodiments, when the instruction is determined to be adescriptor registration instruction according to an operation code ofthe decoded first processing instruction, the corresponding descriptormay be created by a tensor control module in a control circuit accordingto the registration parameter in the first processing instruction.

In some embodiments, the first storage area of the content of thedescriptor in the descriptor storage space and the second storage areaof the tensor data indicated by the descriptor in the data storage spacemay be determined first.

For example, if at least one of the storage areas has been preset, thefirst storage area and/or the second storage area may be directlydetermined. For example, it is preset that the content of the descriptorand the content of the tensor data are stored in a same storage space,and the storage address of the content of the descriptor correspondingto the identifier TR0 of the descriptor is ADDR32-ADDR63, and thestorage address of the content of the tensor data is ADDR64-ADDR1023,then the two addresses can be directly determined as the first storagearea and the second storage area.

In some embodiments, if there is no preset storage area, by the tensorcontrol module, the first storage area may be allocated in thedescriptor storage space for the content of the descriptor, and thesecond storage area may be allocated in the data storage space for thecontent of the tensor data, which is not limited in the presentdisclosure.

In some embodiments, according to the shape of the tensor in theregistration parameter and the data address of the second storage area,the correspondence between the shape of the tensor and the address canbe established to determine the content of the descriptor, so that thecorresponding data address can be determined according to the content ofthe descriptor during data processing. The second storage area can beindicated by the content of the descriptor, and the content of thedescriptor can be stored in the first storage area to complete theregistration process of the descriptor.

For example, for the tensor data 23 shown in FIG. 2, the registrationparameter may include the start address PA_start (base address) of thedata storage space 21, an offset 25 (offset_x) in the X-axis direction,and an offset 24 (offset v) in the Y-axis direction, the size in theX-axis direction (size_x), and the size in the Y-axis direction (assize_y). Based on the parameters, the content of the descriptor can bedetermined according to the formula

$\begin{matrix}{{X{direction}:{ori\_ x}},{offset\_ x},{size\_ x}} \\{{Y{direction}:{ori\_ y}},{offset\_ y},{size\_ y}} \\{PA\_ start}\end{matrix}$

and stored in the first storage area, thereby completing theregistration process of the descriptor.

By adopting the above-mentioned method provided by the presentdisclosure, the descriptor can be automatically created according to thedescriptor registration instruction, and the correspondence between thetensor data indicated by the descriptor and the data address can beimplemented, so that the data address can be obtained through thecontent of the descriptor during data processing, and the data accessefficiency of the processor can be improved.

In some embodiments, the control circuit is further configured to:

when the first processing instruction is a descriptor releaseinstruction, obtain the identifier of the descriptor in the firstprocessing instruction; and

according to the identifier of the descriptor, through the tensorcontrol module, release a first storage area storing the content ofdescriptor in the descriptor storage space and a second storage areastoring the tensor data in the data storage space.

For example, the descriptor release instruction may be used to release(delete) the descriptor in the descriptor storage space to free up thespace occupied by the descriptor. The instruction may include at leastthe identifier of the descriptor.

In some embodiments, when the instruction is determined to be thedescriptor release instruction according to the operation code of thedecoded first processing instruction, the corresponding descriptor maybe released through the tensor control module in the control circuitaccording to the identifier of the descriptor in the first processinginstruction.

In some embodiments, according to the identifier of the descriptor, thestorage area of the descriptor in the descriptor storage space and thestorage area of the content of the tensor data in the data storage spaceindicated by the descriptor can be released by the tensor controlmodule, so that each storage area occupied by the descriptor isreleased.

By adopting the above-mentioned method provided by the presentdisclosure, the space occupied by the descriptor can be released afterthe descriptor is used, thus the limited storage resources can be reusedand the efficiency of resource utilization is improved.

In some embodiments, the control circuit is further configured to:

when the first processing instruction is a descriptor modificationinstruction, obtain a modification parameter of the descriptor in thefirst processing instruction, where the modification parameter includesat least one of the identifier of the descriptor, tensor shape to bemodified, and content of the tensor data indicated by the descriptor:

determine content to be updated of the descriptor by the tensor controlmodule according to the modified parameter of the descriptor; and

update the content of the descriptor in the descriptor storage space bythe tensor control module according to the content to be updated.

For example, the descriptor modification instruction can be used tomodify various parameters of the descriptor, such as the identifier, theshape of the tensor, and the like. The descriptor modificationinstruction may include a modification parameter including at least oneof the identifier of the descriptor, a modified shape of the tensor, andthe modified tensor data. The present disclosure does not limit thespecific content of the modification parameter.

In some embodiments, when the instruction is determined as thedescriptor modification instruction according to the operation code ofthe decoded first processing instruction, the control circuit maydetermine the content to be updated of the descriptor according to themodification parameter in the first processing instruction through thetensor control module. For example, the dimension of a tensor may bechanged from 3 dimensions to 2 dimensions, and the size of a tensor inone or more dimension directions may be also changed.

In some embodiments, after the content to be updated is determined, thetensor control module may update the content of the descriptor in thedescriptor storage space in order to modify the content of thedescriptor to indicate the shape of the modified tensor data. Thepresent disclosure does not limit the scope of the updated content andthe specific updating method.

By adopting the above-mentioned method provided by the presentdisclosure, when the tensor data indicated by the descriptor changes,the descriptor is directly modified to maintain the correspondencebetween the descriptor and the tensor data, which improves theefficiency of resource utilization.

In some embodiments, the control circuit further includes a dependencydetermining module, where the control circuit is further configured to:

determine whether there is a second processing instruction that has adependency relationship with the first processing instruction accordingto the identifier of the descriptor, wherein the second processinginstruction is prior to the first processing instruction in aninstruction queue, wherein an operand of the second processinginstruction has the same identifier of the descriptor; and

block or cache the first processing instruction when there is the secondprocessing instruction that has a dependency relationship with the firstprocessing instruction.

For example, after the descriptor is set, a dependency determiningmodule may be set in the control circuit to determine the dependencybetween instructions according to the descriptor. In some embodiments, adependency between two instructions may indicate relative executionorder of the instructions. For example, if instruction A dependents frominstruction B, instruction B has to be executed prior to instruction A.Accordingly, if the operand of the decoded first processing instructionincludes the identifier of the descriptor, whether there is aninstruction, among pre-instructions of the first processing instructionthat has to be executed before the first processing instruction may bedetermined by the dependency determining module in the control circuit.A pre-instruction is an instruction prior to the first processinginstruction in an instruction queue.

In this case, for instructions in the instruction queue prior to thefirst processing instruction, i.e., pre-instructions, the dependencydetermining module may search for the second processing instruction withthe identifier of the descriptor in the operand, and determine whetherthe second processing instruction has a dependency relationship with thefirst processing instruction.

For example, if the first processing instruction is an operationinstruction for the descriptor TR0, and the second processinginstruction is a writing instruction for the descriptor TR0, during theexecution of the second processing instruction, the first processinginstruction cannot be executed, and thus the first processinginstruction depends on the second processing instruction. For anotherexample, if the second processing instruction includes a synchronizationinstruction (sync) for the first processing instruction, the firstprocessing has to be executed after the second processing instruction isexecuted completely, and thus the first processing instruction againdepends on the second processing instruction.

In some embodiments, if there is a second processing instruction thathas a dependency relationship with the first processing instruction, thefirst processing instruction may be blocked, in other words, theexecution of the first processing instruction and other instructionsafter the first processing instruction can be suspended until the secondprocessing instruction is executed completely, and then the firstprocessing instruction and other instructions after the first processinginstruction can be executed.

In some embodiments, if there is a second processing instruction thathas a dependency relationship with the first processing instruction, thefirst processing instruction may be cached, in other words, the firstprocessing instruction is stored in a preset caching space withoutaffecting the execution of other instructions. After the execution ofthe second processing instruction is completed, the first processinginstruction in the caching space is then executed. The presentdisclosure does not limit the particular method for processing the firstprocessing instruction when there is a second processing instructionthat has a dependency relationship with the first processinginstruction.

By adopting the above-mentioned method provided by the presentdisclosure, a dependency between instructions caused by the instructiontype and/or by the synchronization instruction is determined by thedependency determining module, thereby ensuring the execution order ofthe instructions, and the accuracy of data processing.

In some embodiments, the control circuit is further configured to:

determine a current state of the descriptor according to the identifierof the descriptor by the tensor control module, where the current stateof the descriptor includes an operable state or an inoperable state; and

block or cache the first processing instruction when the descriptor isin the inoperable state.

For example, a correspondence table for the state of the descriptor maybe stored in a tensor control module to display the current state of thedescriptor, where the state of the descriptor includes the operablestate or the inoperable state.

In some embodiments, in the case where the pre-instructions of the firstprocessing instruction are processing the descriptor (for example,writing or reading), the tensor control module may set the current stateof the descriptor to the inoperable state. Under the inoperable state,the first processing instruction cannot be executed, and will be blockedor cached. Conversely, in the case where there is no pre-instructionthat is currently processing the descriptor, the tensor control modulemay set the current state of the descriptor to the operable state. Underthe operable state, the first processing instruction can be executed.

In some embodiments, when the content of the descriptor is stored in aTR (Tensor Register), the usage of TR may be stored in thecorrespondence table for the state of the descriptor of the tensorcontrol module to determine whether the TR is occupied or released, soas to manage limited register resources.

By adopting the above-mentioned method provided by the presentdisclosure, the dependency between instructions can be determinedaccording to the state of the descriptor, thereby ensuring the executionorder of the instructions, and accuracy of data processing.

In some embodiments, the first processing instruction includes a dataaccess instruction, and the operand includes source data and targetdata.

The control circuit is further configured to:

when at least one of the source data and the target data includes anidentifier of a descriptor, determine, by the tensor control module, astorage space of the descriptor;

obtain the content of the descriptor from the descriptor storage space;and

send the content of the descriptor and the first processing instructionto the execution circuit.

The execution circuit is configured to:

according to the content of the received descriptor and the firstprocessing instruction, determine a first data address of the sourcedata and/or a second data address of the target data, respectively; and

read data from the first data address and write the data to the seconddata address.

For example, the operand of the data access instruction includes sourcedata and target data, and the operand of the data access instruction isused to read data from the data address of the source data and write thedata to the data address of the target data. When the first processinginstruction is a data access instruction, the tensor data can beaccessed through the descriptor. When at least one of the source dataand the target data of the data access instruction includes theidentifier of the descriptor, the descriptor storage space of thedescriptor may be determined by tensor control module.

In some embodiments, if the source data includes an identifier of afirst descriptor, and the target data includes an identifier of a seconddescriptor, the control circuit may determine a first descriptor storagespace of the first descriptor and a second descriptor storage space ofthe second descriptor through the tensor control module, and the controlcircuit may read the content of the first descriptor and the content ofthe second descriptor from the first descriptor storage space and thesecond descriptor storage space, respectively, and the control circuitmay send the content of the first descriptor and the content of thesecond descriptor to the execution circuit. After receiving the contentof the first descriptor and the content of the second descriptor, theexecution circuit may compute the first data address of the source dataand the second data address of the target data, respectively. Finally,data is read from the first data address and written to the second dataaddress to complete the entire access process.

For example, the source data may be off-chip data to be read, and theidentifier of the first descriptor of the source data is 1. The targetdata is a piece of storage space on the chip, and the identifier of thesecond descriptor of the target data is 2. The control circuit 11 a mayrespectively obtain the content D1 of the first descriptor and thecontent D2 of the second descriptor obtained from the descriptor storagespace according to the identifier 1 of the first descriptor of thesource data and the identifier 2 of the second descriptor of the targetdata. Then the control circuit 11 a may send the content D1 of the firstdescriptor, the content D2 of the second descriptor, and the firstprocessing instruction to the execution circuit 12 a. In someembodiments, the content D1 of the first descriptor and the content D2of the second descriptor can be structured as follows:

$D1:\{ \begin{matrix}{{X{{direction}:{ori\_ x1}}},{offset\_ x1},{size\_ x1}} \\{{Y{{direction}:{ori\_ y1}}},{offset\_ y1},{{size\_ y}1}} \\{PA\_ start1}\end{matrix} $ $D2:\{ \begin{matrix}{{X{direction}:{ori\_ x2}},{offset\_ x2},{size\_ x2}} \\{{Y{direction}:{ori\_ y2}},{offset\_ y2},{size\_ y2}} \\{PA\_ start2}\end{matrix} $

According to the content D1 of the first descriptor and the content D2of the second descriptor, the execution circuit 12 a may obtain a startphysical address PA3 of the source data and a start physical address PA4of the target data, respectively, which can be structured as follows insome embodiments:

PA3=PA_start1+(offset_y1−1)*ori_x1+offset_x1

PA4=PA_start2+(offset_y2−1)*ori_x2+offset_x2

According to the start physical address PA3 of the source data and thestart physical address PA4 of the target data, and the content D1 of thefirst descriptor and the content D2 of the second descriptor, theexecution circuit 12 a may determine the first data address and thesecond data address, respectively, read data from the first data addressand write the data to the second data address (via an IO path), so as toload tensor data indicated by D1 into a storage space indicated by D2.

In some embodiments, if only the source data includes the identifier ofthe first descriptor, the control circuit may determine the firstdescriptor storage space of the first descriptor through the tensorcontrol module. Then the control circuit may read the content of thefirst descriptor from the first descriptor storage space and send thecontent of the first descriptor to the execution circuit. Afterreceiving the content of the first descriptor, the execution circuit maycompute the first data address of the source data; according to thesecond data address of the target data in the operand of theinstruction, the execution circuit may read data from the first dataaddress and write the data to the second data address, therebycompleting the entire access process.

In some embodiments, if only the target data includes the identifier ofthe second descriptor, the control circuit may determine the seconddescriptor storage space of the second descriptor through the tensorcontrol module. Then the control circuit may read the content of thesecond descriptor from the second descriptor storage space and send thecontent of the second descriptor to the execution circuit. Afterreceiving the content of the second descriptor, the execution circuitmay compute the second data address of the target data; according to thefirst data address of the source data in the operand of the instruction,the execution circuit may read data from the first data address andwrite the data to the second data address, thereby completing the entireaccess process.

By adopting the above-mentioned method provided by the presentdisclosure, the descriptor can be used to complete the data access. Inthis way, there is no need to provide the data address by theinstructions during each data access, thereby improving data accessefficiency.

In some embodiments, the first processing instruction includes anoperation instruction, where the control circuit is configured to:

determine a data address of data corresponding to an operand of thefirst processing instruction in the data storage space according to thecontent of the received descriptor and the first processing instruction;and

execute an operation corresponding to the first processing instructionaccording to the data address.

For example, when the first processing instruction is an operationinstruction, the operation of tensor data can be implemented by thedescriptor. When the operand of the operation instruction includes theidentifier of the descriptor, the descriptor storage space of thedescriptor can be determined by the tensor control module. Then thecontent of the descriptor is read from the descriptor storage space andsent to the execution circuit. After receiving the content of thedescriptor, the execution circuit may determine the data addresscorresponding to the operand, and then read data from the data addressfor operations, so as to complete the entire operation process. Byadopting the above-mentioned method, the descriptor can be used to readdata during operations, and there is no need to provide the data addressby instructions, thereby improving data operation efficiency.

According to the data processing method provided in the embodiments ofthe present disclosure, the descriptor indicating the shape of thetensor is introduced, so that the data address can be determined by thedescriptor during the execution of the data processing instruction. Theinstruction generation method is simplified from the hardware side,thereby reducing the complexity of data access and improving the dataaccess efficiency of the processor.

In some embodiments, the present disclosure provides an artificialintelligence chip including the above-mentioned data processingapparatus.

In some embodiments, the present disclosure provides a board cardincluding the above-mentioned artificial intelligence chip.

FIG. 4 shows a block diagram of a board card according to an embodimentof the present disclosure. As shown in FIG. 4, in addition to theabove-mentioned artificial intelligence chip 389, the board card mayfurther include other components, including but not limited to: astorage device 390, an interface apparatus 391, and a control device392.

The storage device 390 is connected to the artificial intelligence chipthrough a bus, and is configured to store data. The storage device 390may include a plurality of groups of storage units 393, where each groupof the storage units is connected with the artificial intelligence chipthrough a bus. The descriptor storage space and data storage spacedescribed in this disclosure may be part of the storage device 390. Itcan be understood that each group of the storage units may be DDR SDRAM(Double Data Rate Synchronized Dynamic Random Access Memory).

DDR can double a speed of SDRAM without increasing a clock rate. DDRallows reading data on rising and falling edges of the clock pulse. DDRis twice as fast as standard SDRAM. In an embodiment, the storage devicemay include 4 groups of the storage units, where each group of thestorage units may include a plurality of DDR4 particles (chips). In anembodiment, the inner part of the artificial intelligence chip mayinclude four 72-bit DDR4 controllers, in which 64 bits of the four72-bit DDR4 controllers are used for data transfer, and 8 bits of thefour 72-bit DDR4 controllers are used for ECC check. It can beunderstood that when DDR4-3200 particles are used in each group of thestorage units, the theoretical bandwidth of data transfer can reach25600 MB/s.

In an embodiment, each group of the storage units may include aplurality of DDR SDRAMs arranged in parallel. DDR can transmit datatwice in one clock cycle. A controller for controlling DDR is providedin the artificial intelligence chip, where the controller is used forcontrolling the data transfer and data storage of each storage unit.

The interface apparatus is electrically connected to the artificialintelligence chip, where the interface apparatus is configured toimplement data transfer between the artificial intelligence chip and anexternal equipment (such as a server or a computer). For example, in anembodiment, the interface apparatus may be a standard PCIE interface,and data to be processed is transferred from the server to the chipthrough the standard PCIE interface to realize data transfer.Preferably, when a PCIE 3.0×16 interface is used for data transfer, thetheoretical bandwidth can reach 16000 MB/s. In another embodiment, theinterface apparatus may further include other interfaces. The presentdisclosure does not limit the specific types of the interfaces, as longas the interface units can implement data transfer. In addition, thecomputation result of the artificial intelligence chip is stilltransmitted back to an external equipment (such as a server) by theinterface apparatus.

The control device is electrically connected to the artificialintelligence chip, where the control device is configured to monitor thestate of the artificial intelligence chip. Specifically, the artificialintelligence chip may be electrically connected to the control devicethrough an SPI interface, where the control device may include an MCU(Micro Controller Unit). The artificial intelligence chip may include aplurality of processing chips, a plurality of processing cores, or aplurality of processing circuits, and is capable of driving a pluralityof loads. Therefore, the artificial intelligence chip can be indifferent working states such as multi-load state and light-load state.The operations of a plurality of processing chips, a plurality ofprocessing cores and/or a plurality of processing circuits in theartificial intelligence chip can be regulated by the control device.

In some embodiments, the present disclosure provides an electronicdevice including the artificial intelligence chip. The electronic deviceincludes a data processing apparatus, a robot, a computer, a printer, ascanner, a tablet computer, an intelligent terminal, a mobile phone, anautomobile data recorder, a navigator, a sensor, a webcam, a cloudserver, a camera, a video camera, a projector, a watch, an earphone, amobile storage, a wearable apparatus, a transportation means, ahousehold electrical appliance, and/or a medical apparatus.

The transportation means may include an airplane, a ship, and/or avehicle. The household electrical appliance may include a television, anair conditioner, a microwave oven, a refrigerator, an electric ricecooker, a humidifier, a washing machine, an electric lamp, a gas cooker,and a range hood. The medical apparatus may include a nuclear magneticresonance spectrometer, a B-ultrasonic scanner, and/or anelectrocardiograph.

A1. A data processing apparatus, comprising a control circuit and anexecution circuit, wherein the control circuit includes a tensor controlmodule, and the control circuit is configured to:

when an operand of a decoded first processing instruction includes anidentifier of a descriptor, determine a descriptor storage spacecorresponding to the descriptor by the tensor control module accordingto the identifier of the descriptor, wherein the descriptor indicates ashape of a tensor data;

obtain content of the descriptor from the descriptor storage space; and

send the content of the descriptor and the first processing instructionto the execution circuit, for the execution circuit to execute the firstprocessing instruction according to the content of the descriptor.

A2. The data processing apparatus of A1, wherein the execution circuitis configured to:

determine a data address of the tensor data corresponding to the operandof the first processing instruction in a data storage space according tothe received content of the descriptor and the first processinginstruction; and

execute the first processing instruction according to the data address.

A3. The data processing apparatus of A2, wherein the execution circuitis further configured to:

when the first processing instruction is a descriptor registrationinstruction, obtain a registration parameter of the descriptor in thefirst processing instruction, wherein the registration parameterincludes at least one of the identifier of the descriptor, the shape ofthe tensor, and content of the tensor data indicated by the descriptor;

determine, by the tensor control module, a first storage area in thedescriptor storage space for storing the content of the descriptor, anda second storage area in the data storage space for storing the contentof the tensor data indicated by the descriptor;

determine the content of the descriptor according to the registrationparameter of the descriptor and the second storage area, thusestablishing a correspondence between the descriptor and the secondstorage area; and

store the content of the descriptor into the first storage area.

A4. The data processing apparatus of any one of A1-A3, wherein thecontrol circuit is further configured to:

when the first processing instruction is a descriptor releaseinstruction, obtain an identifier of the descriptor in the firstprocessing instruction; and

release, by the tensor control module, a first storage area storing thecontent of descriptor in the descriptor storage space and a secondstorage area storing the tensor data in the data storage space,according to the identifier of the descriptor.

A5. The data processing apparatus of any one of A1-A4, wherein thecontrol circuit is further configured to:

when the first processing instruction is a descriptor modificationinstruction, obtain a modification parameter of the descriptor in thefirst processing instruction, wherein the modification parameterincludes at least one of the identifier of the descriptor, a tensorshape to be modified, and the content of the tensor data referenced bythe descriptor;

determine content to be updated of the descriptor by the tensor controlmodule according to the modified parameter of the descriptor; and

update the content of the descriptor in the descriptor storage space bythe tensor control module according to the content to be updated.

A6. The data processing apparatus of any one of A1-A5, wherein thecontrol circuit further includes a dependency determining module,wherein the control circuit is further configured to:

determine whether there is a second processing instruction that has adependency relationship with the first processing instruction accordingto the identifier of the descriptor, wherein the second processinginstruction is prior to the first processing instruction in aninstruction queue, wherein an operand of the second processinginstruction has the same identifier of the descriptor; and

block or cache the first processing instruction when there is a secondprocessing instruction that has a dependency relationship with the firstprocessing instruction.

A7. The data processing apparatus of any one of A1-A5, wherein thecontrol circuit is further configured to:

determine a current state of the descriptor according to the identifierof the descriptor by the tensor control module, wherein the currentstate of the descriptor includes an operable state or an inoperablestate; and

block or cache the first processing instruction when the descriptor isin the inoperable state.

A8. The data processing apparatus of A2, wherein the first processinginstruction includes a data access instruction, and the operand includessource data and target data, wherein the control circuit is configuredto:

when at least one of the source data and the target data includes anidentifier of a descriptor, by the tensor control module, determine astorage space of the descriptor;

obtain content of the descriptor from the descriptor storage space; and

send the content of the descriptor and the first processing instructionto the execution circuit;

-   -   wherein the execution circuit is configured to:

according to the content of the received descriptor and the firstprocessing instruction, determine a first data address of the sourcedata and/or a second data address of the target data, respectively; and

read data from the first data address and write the data to the seconddata address.

A9. The data processing apparatus of A2, wherein the first processinginstruction includes an operation instruction, wherein the executioncircuit is configured to:

determine a data address of data corresponding to the operand of thefirst processing instruction in the data storage space according to thereceived descriptor content and the first processing instruction; and

execute an operation corresponding to the first processing instructionaccording to the data address.

A10. The data processing apparatus of any one of A1-A9, wherein thedescriptor is used to indicate a shape of N-dimensional tensor data,wherein N is an integer greater than or equal to 0, and the content ofthe descriptor includes at least one shape parameter indicating theshape of the tensor data.

A11. The data processing apparatus of A10, wherein the descriptor isalso used to indicate an address of N-dimensional tensor data, and thecontent of the descriptor further includes at least one addressparameter indicating the address of the tensor data.

A12. The data processing apparatus of A11, wherein the address parameterof the tensor data includes a base address of a datum point of thedescriptor in the data storage space of the tensor data, wherein theshape parameter of the tensor data includes at least one of following:

a size of the data storage space in at least one of N dimensions, a sizeof the storage area in at least one of N dimensions, an offset of thestorage area in at least one of N dimensions, a position of at least twovertices at diagonal positions in N dimensions relative to the datumpoint, and a mapping relationship between a data description position ofthe tensor data indicated by the descriptor and the data address of thetensor data indicated by the descriptor.

A13. The data processing apparatus of any one of A1-A12, wherein thecontrol circuit is further configured to:

decode the received first processing instruction to obtain the decodedfirst processing instruction; wherein

the decoded first processing instruction includes an operation code andone or more operands, wherein the operation code is used to indicate aprocessing type corresponding to the first processing instruction.

A14. The data processing apparatus of any one of A2-A13, wherein thedescriptor storage space is a storage space in an internal memory of thecontrol circuit, and the data storage space is a storage space in aninternal memory of the control circuit or a storage space in an externalmemory connected to the control circuit.

A15. An artificial intelligence chip, comprising the data processingapparatus of any one of A1-A14.

A16. An electronic device, comprising the artificial intelligence chipof A15.

A17. A board card, comprising a storage device, an interface apparatus,a control device, and the artificial intelligence chip of A15, wherein

the artificial intelligence chip is connected to the storage device, thecontrol device, and the interface apparatus, respectively;

the storage device is configured to store data;

the interface apparatus is configured to implement data transfer betweenthe artificial intelligence chip and an external equipment; and

the control device is configured to monitor a state of the artificialintelligence chip.

A18. The board card of A17, wherein

the storage device includes a plurality of groups of storage units,wherein each group of the storage units is connected with the artificialintelligence chip by a bus, and the storage units are DDR SDRAMs;

the chip includes a DDR controller configured to control data transferand data storage of each storage unit; and

the interface apparatus is a standard PCIE interface.

With the continuous development of AI (Artificial Intelligence)technology, the amount of data and data dimensions that need to beprocessed are increasing. In related arts, processors usually determinethe data address by obtaining parameters of instructions, and then readand use data according to the data address. Therefore, relevantparameters (such as the relationship between data or between datadimensions, etc.) are used for data access, while generatinginstructions and sending the instructions to processors to complete thedata access. The disclosed method reduces the processing efficiency ofthe processors.

One aspect of the present disclosure provides a data processingapparatus. FIG. 1b 1 shows a block diagram of a data processingapparatus according to an embodiment of the present disclosure. As shownin FIG. 1b 1, the data processing apparatus includes a control circuit11 b and an execution circuit 12 b, where the control circuit 11 bincludes a tensor control module 111 b, and the control circuit 11 b maybe configured to:

determine that an operand of a first processing instruction includes anidentifier of a descriptor, wherein content of the descriptor indicatesa shape of tensor data on which the first processing instruction is tobe executed;

determine a descriptor storage space corresponding to the descriptoraccording to the identifier of the descriptor:

obtain the content of the descriptor from the descriptor storage space;and

determine a data address of the tensor data to be used as the operand ofthe first processing instruction in the data storage space according tothe content of the descriptor.

The execution circuit is configured to execute the first processinginstruction on the tensor data obtained from the data address.

According to the data processing apparatus provided in the presentdisclosure, by introducing the descriptor indicating the shape of thetensor and configuring the tensor control module in the control circuit,the content of the descriptor may be obtained by the tensor controlmodule when the operand of the decoded processing instruction includesthe identifier of the descriptor. Based on the identifier of thedescriptor, the data address may be determined by the tensor controlmodule, so that instructions can be executed on the date obtained fromsuch data address. As a result, the complexity of data access can bereduced and the efficiency of data access can be improved.

For example, the data processing apparatus may be a processor, where theprocessor may include a general-purpose processor (such as a CPU(central processing unit), a GPU (graphics processor)) and a dedicatedprocessor (such as an AI processor, a scientific computing processor, ora digital signal processor, etc.). This disclosure does not limit thetype of the processor.

In some embodiments, the data processing apparatus includes the controlcircuit 11 b and the execution circuit 12 b, where the control circuit11 b is configured to control devices, for example, the control circuit11 b may read an instruction from a memory or an externally inputinstruction, decode the instruction, and send a micro-operation controlsignal to corresponding components. The execution circuit 12 b isconfigured to execute specific instructions, where the execution circuit12 b may be, for example, an ALU (arithmetic and logic unit), an MAU(memory access unit), an NFU (neural functional unit), etc. The presentdisclosure does not limit the specific hardware type of the executioncircuit 12 b.

In some embodiments, data processed by the data processing apparatus mayinclude N-dimensional tensor data (N is an integer greater than or equalto 0, for example, N=1, 2, or 3). In the present disclosure, adescriptor (tensor descriptor) is introduced to indicate the shape ofthe tensor (N-dimensional tensor data), where the value of N can bedetermined according to a count of dimensions (orders) of the tensordata, and can also be set according to the usage of the tensor data. Forexample, when the value of N is 3, the tensor data is 3-dimensionaltensor data, and the descriptor can be used to indicate the shape (suchas offset, size, etc.) of the 3-dimensional tensor data in threedimensions. It should be understood that those skilled in the art canset the value of N according to actual needs, which is not limited inthe present disclosure.

In some embodiments, the descriptor may include an identifier, content,and the like. The identifier of the descriptor may be used todistinguish the descriptor from other descriptors. For example, theidentifier may be a serial number. The content of the descriptor mayinclude at least one shape parameter (such as a size of each dimensionof the tensor, etc.) representing the shape of the tensor data, and mayalso include at least one address parameter (such as a base address of adatum point) representing an address of the tensor data. The presentdisclosure does not limit the specific parameters included in thecontent of the descriptor.

By using the descriptor to describe the tensor data, the shape of thetensor data can be indicated, and related information such as therelationship among a plurality of pieces of tensor data can bedetermined accordingly, thus improving the efficiency of accessingtensor data.

In some embodiments, a tensor control module 111 b may be provided inthe control circuit 11 b to implement operations associated with thedescriptor, where the operations may include registration, modification,and release of the descriptor, reading and writing of the content of thedescriptor, etc. The tensor control module 111 b may be, for example, aTIU (Tensor interface Unit). The present disclosure does not limit thespecific hardware type of the tensor control module. In this way, theoperations associated with the descriptor can be implemented by specialhardware, which further improves the access efficiency of tensor data.

In some embodiments, when the data processing apparatus receives theprocessing instruction, the data processing apparatus decodes theprocessing instruction through the control circuit 11 b. The controlcircuit 11 b is further configured to decode the received firstprocessing instruction to obtain a decoded first processing instruction.The decoded first processing instruction includes an operation code andone or more operands, and the operation code is used to indicate aprocessing type corresponding to the first processing instruction.

In the present disclosure, after the first processing instruction isdecoded by the control circuit 11 b, a decoded first processinginstruction (microinstruction) may be obtained. The first processinginstruction may include a data access instruction, an operationinstruction, a descriptor management instruction, a synchronizationinstruction, and the like. The present disclosure does not limit thespecific type of the first processing instruction and the specificmanner of decoding.

The decoded first processing instruction includes an operation code andone or more operands, where the operation code is used to indicate aprocessing type corresponding to the first processing instruction, andthe operand is used to indicate data to be processed. For example, theinstruction can be represented as: Add; A; B, where Add is an operationcode, A and B are operands, and the instruction is used to add A and B.The present disclosure does not limit the number of operands involved inthe operation and the format of the decoded instruction.

In some embodiments, the operand of the decoded first processinginstruction includes the identifier of the descriptor. A storage spacein which the descriptor is stored can be determined according to theidentifier of the descriptor. The content (including informationindicating the shape, the address, etc., of tensor data) of thedescriptor can be obtained from the descriptor storage space. Then,according to the content of the descriptor, the data address of the datastorage space corresponding to the operand may be determined by thetensor control module, and the first processing instruction may beexecuted by the tensor control module on the tensor data obtainedaccording to the data address.

In other words, when the operand of the first processing instructionincludes an identifier of the descriptor, the tensor control module mayobtain the content of the descriptor from the descriptor storage spaceaccording to the identifier of the descriptor. Then, according to thecontent of the descriptor, the tensor control module may determine adata address of tensor data corresponding to the operand of the firstprocessing instruction in the data storage space. The correspondinginstruction can be executed on the tensor data obtained according to thedata address.

By adopting the above-mentioned method provided by the presentdisclosure, the content of the descriptor can be obtained from thedescriptor storage space, and then the data address can be obtained. Inthis way, it is not necessary to input the address through aninstruction during each data access, thus improving the data accessefficiency of the processor.

In some embodiments, the identifier and content of the descriptor can bestored in the descriptor storage space, where the descriptor storagespace can be a storage space in an internal memory (such as a register,an on-chip SRAM, or other medium cache, etc.) of the control circuit.Similarly, the data storage space of the tensor data indicated by thedescriptor may also be a storage space in the internal memory (such asan on-chip cache) of the control circuit or a storage space in anexternal memory (an off-chip memory) connected to the control circuit.The data address of the data storage space may be an actual physicaladdress or a virtual address. The present disclosure does not limit aposition of the descriptor storage space and a position of the datastorage space, and the type of the data address.

In some embodiments, the identifier and content of the descriptor, andtensor data indicated by the descriptor can be stored in a same area.For example, a continuous area of an on-chip cache with addressesADDR0-ADDR1023 can be used to store the above information. Within thisarea, addresses ADDR0-ADDR31 can be used to store the identifier of thedescriptor, addresses ADDR32-ADDR63 can be used to store the content ofthe descriptor, and addresses ADDR64-ADDR1023 can be used to store thetensor data indicated by the descriptor. The address ADDR is not limitedto 1 bit or 1 byte, and is an address unit used to represent an address.Those skilled in the art can determine the storage area and the addressthereof according to the specific applications, which is not limited inthe present disclosure.

In some embodiments, the identifier and content of the descriptor, andthe tensor data indicated by the descriptor can be respectively storedin different areas of an internal memory. For example, a register can beused as a descriptor storage space to store the identifier and contentof the descriptor, and an on-chip cache can be used as a data storagespace to store the tensor data indicated by the descriptor.

In some embodiments, a special register (SR) may be provided for thedescriptor, where data in the descriptor may be an immediate number orbe obtained from the special register. When the register is used tostore the identifier and content of the descriptor, a serial number ofthe register can be used to indicate the identifier of the descriptor.For example, if the serial number of the register is 0, the identifierof a descriptor stored in the register is 0. When the descriptor in theregister is valid, an area can be allocated in a caching space (such ascreating a tensor caching unit for each piece of tensor data in thecache) according to a size of tensor data indicated by the descriptorfor storing the tensor data. It should be understood that a presetcaching space may also be used to store the tensor data, which is notlimited in the present disclosure.

In some embodiments, the identifier and content of the descriptor can bestored in an internal memory, and the tensor data indicated by thedescriptor can be stored in an external memory. For example, theidentifier and content of the descriptor may be stored on-chip and thetensor data indicated by the descriptor may be stored off-chip.

In some embodiments, the data address of the data storage spaceidentified by the descriptor may be a fixed address. For example, aseparate data storage space may be allocated for tensor data, where astart address of each piece of tensor data in the data storage spacecorresponds to one identifier of the descriptor. In this case, thecontrol circuit can determine the data address of the data correspondingto the operand via the tensor control module according to the content ofthe descriptor, and then execute the first processing instruction.

In some embodiments, when the data address of the data storage spacecorresponding to the identifier of the descriptor is a variable address,the descriptor may be also used to indicate an address of N-dimensionaltensor data, where the content of the descriptor may further include atleast one address parameter representing the address of the tensor data.For example, if the tensor data is 3-dimensional data, when thedescriptor points to the address of the tensor data, the content of thedescriptor may include an address parameter indicating the address ofthe tensor data, such as a start address of the tensor data; or thecontent of the descriptor may include a plurality of address parametersof the address of the tensor data, such as a start address+addressoffset of the tensor data, or address parameters of the tensor data ineach dimension. Those skilled in the art can set the address parametersaccording to actual needs, which is not limited in the presentdisclosure.

In some embodiments, the address parameter of the tensor data includes abase address of the datum point of the descriptor in the data storagespace of the tensor data, where the base address may vary from differentdatum points. The present disclosure does not limit the selection of thedatum point.

In some embodiments, the base address may include a start address of thedata storage space. When the datum point of the descriptor is a firstdata block of the data storage space, the base address of the descriptoris the start address of the data storage space. When the datum point ofthe descriptor is other data than the first data block in the datastorage space, the base address of the descriptor is the physicaladdress of the data block in the data storage space.

In some embodiments, the shape parameter of the tensor data includes atleast one of the followings: a size of the data storage space in atleast one of the N dimensions, a size of the storage area of the tensordata in at least one of the N dimensions, an offset of the storage areain at least one of the N dimensions, a position of at least two verticesat diagonal positions in the N dimensions relative to the datum point,and a mapping relationship between a data description position of thetensor data indicated by the descriptor and the data address of thetensor data indicated by the descriptor. The data description positionis a mapping position of a point or an area in the tensor data indicatedby the descriptor, for example, if the tensor data is 3-dimensionaldata, the descriptor can use a coordinate (x, y, z) to represent theshape of the tensor data, and the data description position of thetensor data can be represented by the coordinate (x, y, z), and the datadescription position of the tensor data may be a position of a point oran area to which the tensor data is mapped in a 3-dimensional space.

It should be understood that those skilled in the art may select a shapeparameter representing tensor data according to actual conditions, whichis not limited in the present disclosure.

In some embodiments, registration, modification and release operationsof the descriptor can be performed in response to managementinstructions of the descriptor, and corresponding operation codes areset for the management instructions. For example, a descriptor can beregistered (created) through a descriptor registration instruction(TRCreat). As another example, various parameters (shape, address, etc.)of the descriptor can be modified through the descriptor modificationinstruction. As a further example, the descriptor can be released(deleted) through the descriptor release instruction (TRRelease). Thepresent disclosure does not limit the types of the managementinstructions of the descriptor and the operation codes.

In some embodiments, the control circuit is further configured to:

when the first processing instruction is a descriptor registrationinstruction, obtain a registration parameter of the descriptor in thefirst processing instruction, wherein the registration parameterincludes at least one of the identifier of the descriptor, the shape ofthe tensor data, and content of the tensor data referenced by thedescriptor;

determine a first storage area in the descriptor storage space forstoring the content of the descriptor, and a second storage area in thedata storage space for storing the content of the tensor data indicatedby the content of the descriptor;

determine the content of the descriptor indicate the second storagespace, thus establishing a correspondence between the descriptor and thesecond storage area; and

store the content of the descriptor into the first storage area.

For example, the descriptor registration instruction may be used toregister a descriptor, and the instruction may include a registrationparameter of the descriptor. The registration parameter may include atleast one of the identifier (ID) of the descriptor, the shape of thetensor, and the tensor data referenced by the descriptor. For example,the registration parameter may include an identifier TR0 and the shapeof the tensor (a count of dimensions, a size of each dimension, anoffset, a start data address, etc.). The present disclosure does notlimit the specific content of the registration parameter.

In some embodiments, when the instruction is determined to be adescriptor registration instruction according to an operation code ofthe decoded first processing instruction, the corresponding descriptormay be created by the tensor control module in the control circuitaccording to the registration parameter in the first processinginstruction.

In some embodiments, the first storage area in the descriptor storagespace that stores the content of the descriptor and the second storagearea in the data storage space that stores the tensor data indicated bythe descriptor may be determined first.

In some embodiments, if at least one of the storage areas has beenpreset, the first storage area and/or the second storage area may bedirectly determined. For example, it is preset that the content of thedescriptor and the content of the tensor data are stored in a samestorage space, where the storage address of the content of thedescriptor corresponding to the identifier TR0 of the descriptor isADDR32-ADDR63, and the storage address of the content of the tensor datais ADDR64-ADDR1023. Accordingly, these two addresses can be directlydetermined as the first storage area and the second storage area.

In some embodiments, if there is no preset storage area, by the tensorcontrol module, the first storage area may be allocated in thedescriptor storage space for the content of the descriptor, and thesecond storage area may be allocated in the data storage space for thecontent of the tensor data, which is not limited in the presentdisclosure.

In some embodiments, according to the shape of the tensor in theregistration parameter and the data address of the second storage area,the content of the descriptor can be determined to indicate the same.Therefore, during data processing, the corresponding data address can bedetermined according to the content of the descriptor. The content ofthe descriptor can be stored in the first storage area to complete theregistration process of the descriptor.

For example, for the tensor data 23 shown in FIG. 2, the registrationparameter may include the start address PA_start (base address) of thedata storage space 21, an offset 25 (offset_x) in the X-axis direction,and an offset 24 (offset_y) in the Y-axis direction, the size in theX-axis direction (size_x), and the size in the Y-axis direction (assize_y). Based on the parameters, the content of the descriptor can bedetermined according to the formula

$\begin{matrix}{{X{direction}:{ori\_ x}},{offset\_ x},{size\_ x}} \\{{Y{direction}:{ori\_ y}},{offset\_ y},{size\_ y}} \\{PA\_ start}\end{matrix}$

described above and stored in the first storage area, thereby completingthe registration process of the descriptor.

By adopting the above-mentioned method provided by the presentdisclosure, the descriptor can be automatically created according to thedescriptor registration instruction, and the correspondence between thedescriptor and the data address of the tensor data can be implemented,so that the data address can be obtained through the content of thedescriptor during data processing, and the data access efficiency of theprocessor can be improved.

In some embodiments, the control circuit is further configured to:

when the first processing instruction is a descriptor releaseinstruction, obtain the identifier of the descriptor in the firstprocessing instruction; and

release a first storage area storing the content of descriptor in thedescriptor storage space and a second storage area storing the tensordata in the data storage space, according to the identifier of thedescriptor.

For example, the descriptor release instruction may be used to release(delete) the descriptor in the descriptor storage space to free up thespace occupied by the descriptor. The instruction may include at leastthe identifier of the descriptor.

In some embodiments, when the instruction is determined to be thedescriptor release instruction according to the operation code of thedecoded first processing instruction, the corresponding descriptor maybe released by the tensor control module in the control circuitaccording to the identifier of the descriptor in the first processinginstruction.

In some embodiments, according to the identifier of the descriptor, thetensor control module may release the storage area of the descriptor inthe descriptor storage space and the storage area of the content of thetensor data in the data storage space indicated by the descriptor, so asto release each storage area occupied by the descriptor.

By adopting the above-mentioned method provided by the presentdisclosure, the space occupied by the descriptor can be released afterthe descriptor is used, the limited storage resources can be reused,thus the efficiency of resource utilization is improved.

In some embodiments, the control circuit is further configured to:

when the first processing instruction is a descriptor modificationinstruction, obtain a modification parameter of the descriptor in thefirst processing instruction, wherein the modification parameterincludes at least one of the identifier of the descriptor, a modifiedshape of the tensor, and content of the tensor data referenced by thedescriptor;

determine updated content of the descriptor according to themodification parameter of the descriptor; and

update the content of the descriptor in the descriptor storage space orthe content of the tensor data in the data storage space according tothe updated content of the descriptor.

For example, the descriptor modification instruction can be used tomodify various parameters of the descriptor, such as the identifier, theshape of the tensor, and the like. The descriptor modificationinstruction may include a modification parameter including at least oneof the identifier of the descriptor, tensor shape to be modified, andthe content of the tensor data indicated by the descriptor. The presentdisclosure does not limit the specific content of the modificationparameter.

In some embodiments, when the instruction is determined to be adescriptor modification instruction according to the operation code ofthe decoded first processing instruction, the control circuit maydetermine the content of the descriptor to be updated according to themodification parameter in the first processing instruction by the tensorcontrol module. For example, the dimension of a tensor may be changedfrom 3 dimensions to 2 dimensions, and the size of a tensor in one ormore dimension directions may be also changed.

In some embodiments, after the content to be updated is determined, thetensor control module may update the content of the descriptor in thedescriptor storage space and/or the content of tensor data in the datastorage space in order to modify the tensor data and modify the contentof the descriptor to indicate the shape of the modified tensor data. Thepresent disclosure does not limit the scope of the content to be updatedand the specific updating method.

By adopting the above-mentioned method provided by the presentdisclosure, when the tensor data indicated by the descriptor changes,the descriptor can be modified to indicate the changed tensor data,which improves the efficiency of resource utilization.

In some embodiments, the control circuit further includes a dependencydetermining module, wherein the control circuit is further configuredto:

determine whether there is a second processing instruction that has adependency relationship with the first processing instruction accordingto the identifier of the descriptor, wherein the second processinginstruction is prior to the first processing instruction in aninstruction queue, wherein an operand of the second processinginstruction has the same identifier of the descriptor; and

block or cache the first processing instruction when there is the secondprocessing instruction that has the dependency relationship with thefirst processing instruction.

For example, after the descriptor is set, a dependency determiningmodule may be configured in the control circuit to determine thedependency between instructions according to the descriptor. In someembodiments, a dependency between two instructions may indicate relativeexecution order of the instructions. For example, if instruction Adependents from instruction B, instruction B has to be executed prior toinstruction A. Accordingly, if the operand of the decoded firstprocessing instruction includes the identifier of the descriptor,whether there is an instruction, among pre-instructions of the firstprocessing instruction that has to be executed before the firstprocessing instruction may be determined by the dependency determiningmodule in the control circuit. A pre-instruction is an instruction priorto the first processing instruction in an instruction queue.

In this case, for instructions in the instruction queue prior to thefirst processing instruction, i.e., pre-instructions, the dependencydetermining module may search for the second processing instruction withthe same identifier of the descriptor in the operand, and treat thesecond processing instruction as a processing instruction that has adependency relationship with the first processing instruction. If theoperand of the first processing instruction has identifiers of aplurality of descriptors, the dependency relationship corresponding toeach descriptor may be respectively determined.

For example, if the first processing instruction is an operationinstruction for the descriptor TR0, and the second processinginstruction is a writing instruction for the descriptor TR0, the firstprocessing instruction has a dependency relationship with the secondprocessing instruction, and thus during the execution of the secondprocessing instruction, the first processing instruction cannot beexecuted. For another example, if the second processing instructionincludes a synchronization instruction (sync) with the first processinginstruction, the first processing instruction also has a dependencyrelationship with the second processing instruction, and thus the firstprocessing has to be executed after the second processing instruction isexecuted completely.

In some embodiments, if there is a second processing instruction thathas a dependency relationship with the first processing instruction andhas not been processed, the first processing instruction can be blocked,in other words, the execution of the first processing instruction andother instructions after the first processing instruction can besuspended until the second processing instruction is executedcompletely, and then the first processing instruction and otherinstructions after the first processing instruction can be executed.

In some embodiments, if there is a second processing instruction thathas a dependency relationship with the first processing instruction andhas not been processed, the first processing instruction can be cached,in other words, the first processing instruction is stored in a presetcaching space without affecting the execution of other instructions.After the execution of the second processing instruction is completed,the first processing instruction in the caching space is then executed.The present disclosure does not limit the particular method ofprocessing the first processing instruction in this case.

By adopting the above-mentioned method provided by the presentdisclosure, a dependency between instructions caused by the instructiontype and/or by the synchronization instruction is determined by thedependency determining module, thereby ensuring the execution order ofthe instructions, and the accuracy of data processing.

In some embodiments, the control circuit is further configured to:

determine a state of the descriptor according to the identifier of thedescriptor, wherein the state of the descriptor includes an operablestate or an inoperable state; and

block or cache the first processing instruction when the descriptor isin the inoperable state.

For example, a correspondence table for the state of the descriptor maybe stored in a tensor control module to display the current state of thedescriptor, where the state of the descriptor includes the operablestate or the inoperable state.

In some embodiments, in the case where the pre-instructions of the firstprocessing instruction are processing (for example, writing or reading)the descriptor, the tensor control module may set the current state ofthe descriptor to the inoperable state. With the descriptor in theinoperable state, the first processing instruction cannot be executedbut blocked or cached. Conversely, in the case where there is nopre-instruction that is currently processing the descriptor, the tensorcontrol module may set the current state of the descriptor to theoperable state. With the descriptor in the operable state, the firstprocessing instruction can be executed.

In some embodiments, when the content of the descriptor is stored in aTR (Tensor Register), may be stored in the descriptor state look-uptable can further store the usage state of TR As a result, the tensorcontrol module may determine whether the TR is occupied or released,thus managing limited register resources.

By adopting the above-mentioned method provided by the presentdisclosure, the dependency between instructions can be determinedaccording to the state of the descriptor, thereby ensuring the executionorder of the instructions and accuracy of data processing.

In some embodiments, the first processing instruction includes a dataaccess instruction, and the operand includes source data and targetdata.

The control circuit is further configured to:

when at least one of the source data and the target data includes theidentifier of the descriptor, determine the descriptor storage space:

obtain the content of the descriptor from the descriptor storage space;

determine a first data address of the source data or a second dataaddress of the target data according to the content of the descriptor;and

read the source data from the first data address and write the targetdata to the second data address.

For example, the operand of the data access instruction includes sourcedata and target data, and the operand of the data access instruction isused to read data from the data address of the source data and write thedata to the data address of the target data. When the first processinginstruction is a data access instruction, the tensor data can beaccessed through the descriptor. When at least one of the source dataand the target data of the data access instruction includes theidentifier of the descriptor, the descriptor storage space of thedescriptor may be determined by the tensor control module.

In some embodiments, if the source data includes an identifier of afirst descriptor, and the target data includes an identifier of a seconddescriptor, the control circuit may determine a first descriptor storagespace of the first descriptor and a second descriptor storage space ofthe second descriptor through the tensor control module, and the controlcircuit may read the content of the first descriptor and the content ofthe second descriptor from the first descriptor storage space and thesecond descriptor storage space, respectively. And then, according tothe content of the first descriptor and the content of the seconddescriptor, the first data address of the source data and the seconddata address of the target data are respectively determined by thetensor control module. Finally, data is read from the first data addressand written to the second data address to complete the entire accessprocess.

For example, the source data may be off-chip data to be read, and theidentifier of the first descriptor of the source data is TR1. The targetdata is a piece of storage space on the chip, and the identifier of thesecond descriptor of the target data is TR2. The control circuit 11 bmay respectively obtain content D1 of the first descriptor and contentD2 of the second descriptor obtained from the descriptor storage spaceaccording to the identifier 1 of the first descriptor of the source dataand the identifier 2 of the second descriptor of the target data. Insome embodiments, the content D1 of the first descriptor and the contentD2 of the second descriptor can be structured as following formulas:

$D1:\{ \begin{matrix}{{X{direction}:{ori\_ x1}},{offset\_ x1},{size\_ x1}} \\{{Y{direction}:{ori\_ y1}},{offset\_ y1},{{size\_ y}1}} \\{PA\_ start1}\end{matrix} $ $D2:\{ \begin{matrix}{{X{direction}:{ori\_ x2}},{offset\_ x2},{size\_ x2}} \\{{Y{direction}:{ori\_ y2}},{offset\_ y2},{size\_ y2}} \\{PA\_ start2}\end{matrix} $

According to the content D1 of the first descriptor and the content D2of the second descriptor, the control circuit 11 b may obtain a startphysical address PA3 of the source data and a start physical address PA4of the target data via the tensor control module, which can bestructured as follows in some embodiments:

PA3=PA_start1+(offset_y−1)*ori_x1+offset_x1

PA4=PA_start2+(offset_y2−1)*ori_x2+offset_x2

According to the start physical address PA3 of the source data and thestart physical address PA4 of the target data, and the content D1 of thefirst descriptor and the content D2 of the second descriptor, thecontrol circuit 11 b may determine the first data address and the seconddata address through the tensor control module respectively, read datafrom the first data address and write the data to the second dataaddress (via an IO path), so as to load tensor data indicated by D1 intoa storage space indicated by D2.

In some embodiments, if only the source data includes the identifier ofthe first descriptor, the control circuit may determine the firstdescriptor storage space of the first descriptor through the tensorcontrol module. Then the control circuit may read the content of thefirst descriptor from the first descriptor storage space. And then, thecontrol circuit may compute the first data address of the source datausing the tensor control module according to the content of the firstdescriptor. According to the second data address of the target data inthe operand of the instruction, the control circuit may read data fromthe first data address and write the data to the second data address.The entire the entire access process is then finished.

In some embodiments, if only the target data includes the identifier ofthe second descriptor, the control circuit may determine the seconddescriptor storage space of the second descriptor using the tensorcontrol module. Then the control circuit may read the content of thesecond descriptor from the second descriptor storage space. And then,the control circuit may compute the second data address of the targetdata using the tensor control module according to the content of thesecond descriptor. According to the first data address of the sourcedata in the operand of the instruction, the control circuit may readdata from the first data address and write the data to the second dataaddress. The entire access process is then finished.

By adopting the above-mentioned method provided by the presentdisclosure, the descriptor can be used to complete the data access. Inthis way, there is no need to provide the data address by theinstructions during each data access, thereby improving data accessefficiency.

In some embodiments, the first processing instruction includes anoperation instruction, and the control circuit 11 is configured to:

when the first processing instruction is an operation instruction, sendthe data address and the first processing instruction to the executioncircuit, where the execution circuit is configured to:

execute an operation corresponding to the first processing instructionon the tensor data obtained from the data address.

For example, when the first processing instruction is an operationinstruction, the operation of tensor data can be implemented via thedescriptor. When the operand of the operation instruction includes theidentifier of the descriptor, the control circuit may determine thedescriptor storage space of the descriptor via the tensor controlmodule, and read the content of the descriptor from the descriptorstorage space. According to the content of the descriptor, the controlcircuit may compute the address of the data corresponding to the operandusing the tensor control module, and then send the data address and thefirst processing instruction to the execution circuit. After receivingthe data address, the execution circuit may read data from the dataaddress to execute operations. The entire operation process thenconcludes.

For example, for the instruction Add; A; B, if operands A and B includeidentifiers TR3 and TR4 of the descriptors, respectively, the controlcircuit may determine the descriptor storage spaces corresponding to TR1and TR2 using the tensor control module, and the control circuit mayread the content (such as a shape parameter and an address parameter) ofthe descriptor storage spaces. According to the content of thedescriptor, the control circuit may compute a data address of data A anda data address of data B using the tensor control module. For example, adata address 1 of A in a memory is ADDR64-ADDR127, and a data address 2of B in the memory is ADDR1023-ADDR1087. And then, the control circuitmay send the data address 1 and data address 2 and the Add instructionto the execution circuit. The execution circuit can read data fromaddress 1 and address 2 respectively, execute an addition (Add)operation, and obtain an operation result (A+B).

By adopting the above-mentioned method, the descriptor can be used toread data during operations, and there is no need to provide the dataaddress by instructions, thereby improving data operation efficiency.

According to the data processing method provided in the embodiments ofthe present disclosure, the descriptor indicating the shape of thetensor is introduced, so that the data address can be determined basedon the descriptor during the execution of the data processinginstruction. As a result, the instruction generation method issimplified from the hardware side, thereby reducing the complexity ofdata access and improving the data access efficiency of the processor.

In some embodiments, the present disclosure provides an artificialintelligence chip including the above-mentioned data processingapparatus.

FIG. 1b 2 shows a flowchart of a data processing method according to anembodiment of the present disclosure. In some embodiments, the dataprocessing method may be performed by data processing device as shown inFIG. 1. In step 1, control circuit 11 b may determine that an operand ofa first processing instruction includes an identifier of a descriptor.The first processing instruction may be executed on the operand, whichmay include, e.g., a tensor data. The content of the descriptor mayindicate a shape of the tensor data on which the first processinginstruction is to be executed. In step 2, control circuit 11 b maydetermine a descriptor storage space corresponding to the descriptoraccording to the identifier of the descriptor. The descriptor storagespace may store the content of the descriptor. In step 3, controlcircuit 11 b may obtain the content of the descriptor from thedescriptor storage space. In some embodiments, the content of thedescriptor may indicate a data address of the tensor data to be used asthe operand for executing the first processing instruction. In step 4,control circuit 11 b may determine the data address of the tensor datain the data storage space according to the content of the descriptor.The data storage space may be in a same or different physical locationin the memory as the descriptor storage space. The tensor data may bestored at the data address in the data storage space. In step 5,execution circuit 12 b may execute the first processing instruction onthe tensor data obtained from the data address. For example, the controlcircuit 11 b may provide the data address and the first processinginstruction to the execution circuit 12 b, and according to the dataaddress, the execution circuit 12 b can obtain the tensor data stored atthe data address and then perform the first processing instruction onthe tensor data. Descriptions of embodiments above associated with thedata processing device in connection with FIGS. 1-3 are also applicableto the data processor method in connection with FIG. 1b 2, which willnot be repeated here.

A1. A data processing apparatus, comprising a control circuit and anexecution circuit, wherein the control circuit includes a tensor controlmodule and is configured to:

-   -   determine that an operand of a first processing instruction        includes an identifier of a descriptor, wherein content of the        descriptor indicates a shape of tensor data on which the first        processing instruction is to be executed;    -   determine a descriptor storage space corresponding to the        descriptor according to the identifier of the descriptor;    -   obtain the content of the descriptor from the descriptor storage        space; and    -   determine a data address of the tensor data to be used as the        operand of the first processing instruction in the data storage        space according to the content of the descriptor; and

an execution circuit configured to execute the first processinginstruction on the tensor data obtained from the data address.

A2. The data processing apparatus of A1, wherein the control circuit isfurther configured to:

when the first processing instruction is a descriptor registrationinstruction, obtain a registration parameter of the descriptor in thefirst processing instruction, wherein the registration parameterincludes at least one of the identifier of the descriptor, the shape ofthe tensor data, and content of the tensor data referenced by thedescriptor;

determine a first storage area in the descriptor storage space forstoring the content of the descriptor, and a second storage area in thedata storage space for storing the content of the tensor data indicatedby the content of the descriptor;

determine the content of the descriptor indicate the second storagespace, thus establishing a correspondence between the descriptor and thesecond storage area; and

store the content of the descriptor into the first storage area.

A3. The data processing apparatus of A1 or A2, wherein the controlcircuit is further configured to:

when the first processing instruction is a descriptor releaseinstruction, obtain the identifier of the descriptor in the firstprocessing instruction; and

release a first storage area storing the content of descriptor in thedescriptor storage space and a second storage area storing the tensordata in the data storage space, according to the identifier of thedescriptor.

A4. The data processing apparatus of any one of A1-A3, wherein thecontrol circuit is further configured to:

when the first processing instruction is a descriptor modificationinstruction, obtain a modification parameter of the descriptor in thefirst processing instruction, wherein the modification parameterincludes at least one of the identifier of the descriptor, a modifiedshape of the tensor, and content of the tensor data referenced by thedescriptor:

determine updated content of the descriptor according to themodification parameter of the descriptor; and

update the content of the descriptor in the descriptor storage space orthe content of the tensor data in the data storage space according tothe updated content of the descriptor.

A5. The data processing apparatus of any one of A1-A4, wherein thecontrol circuit further includes a dependency determining module,wherein the control circuit is further configured to:

determine whether there is a second processing instruction that has adependency relationship with the first processing instruction accordingto the identifier of the descriptor, wherein the second processinginstruction is prior to the first processing instruction in aninstruction queue, wherein an operand of the second processinginstruction has the same identifier of the descriptor; and

block or cache the first processing instruction when there is the secondprocessing instruction that has the dependency relationship with thefirst processing instruction.

A6. The data processing apparatus of any one of A1-A5, wherein thecontrol circuit is further configured to:

determine a state of the descriptor according to the identifier of thedescriptor, wherein the state of the descriptor includes an operablestate or an inoperable state; and

block or cache the first processing instruction when the descriptor isin the inoperable state.

A7. The data processing apparatus of any one of A1-A6, wherein the firstprocessing instruction includes a data access instruction, and theoperand includes source data and target data, wherein the controlcircuit is further configured to:

when at least one of the source data and the target data includes theidentifier of the descriptor, determine the descriptor storage space;

obtain the content of the descriptor from the descriptor storage space;

determine a first data address of the source data or a second dataaddress of the target data according to the content of the descriptor;and

read the source data from the first data address and write the targetdata to the second data address.

A8. The data processing apparatus of any one of A1-A7, the firstprocessing instruction includes an operation instruction, wherein thecontrol circuit is configured to:

when the first processing instruction is an operation instruction, sendthe data address and the first processing instruction to the executioncircuit, wherein the execution circuit is configured to execute anoperation corresponding to the first processing instruction according tothe data address.

A9. The data processing apparatus of any one of A1-A8, wherein thedescriptor is used to indicate a shape of N-dimensional tensor data,where N is an integer greater than or equal to 0, and the content of thedescriptor includes at least one shape parameter indicating the shape ofthe tensor data.

A10. The data processing apparatus of A9, wherein the descriptor is alsoused to indicate an address of N-dimensional tensor data, and thecontent of the descriptor further includes at least one addressparameter indicating the address of the tensor data.

A11. The data processing apparatus of A10, wherein the address parameterof the tensor data includes a base address of a datum point of thedescriptor in the data storage space of the tensor data, wherein theshape parameter of the tensor data includes at least one of following:

a size of the data storage space in at least one of N dimensions, a sizeof the storage area in at least one of N dimensions, an offset of thestorage area in at least one of N dimensions, a position of at least twovertices at diagonal positions in N dimensions relative to the datumpoint, and a mapping relationship between a data description position ofthe tensor data indicated by the descriptor and the data address of thetensor data indicated by the descriptor.

A12. The data processing apparatus of any one of A1-A11, wherein thecontrol circuit is further configured to:

decode the received first processing instruction to obtain the decodedfirst processing instruction; wherein

-   -   the decoded first processing instruction includes an operation        code and one or more operands, where the operation code is used        to indicate a processing type corresponding to the first        processing instruction.

A13. The data processing apparatus of any one of A1-A12, wherein thedescriptor storage space is a storage space in an internal memory of thecontrol circuit, and the data storage space is a storage space in aninternal memory of the control circuit or a storage space in an externalmemory connected to the control circuit.

A14. An artificial intelligence chip comprising the data processingapparatus of any one of A1-A13.

A15. An electronic device comprising the artificial intelligence chip ofA14.

A16. A board card comprising a storage device, an interface apparatus, acontrol device, and the artificial intelligence chip of A14, wherein

the artificial intelligence chip is connected to the storage device, thecontrol device, and the interface apparatus, respectively;

the storage device is configured to store data;

the interface apparatus is configured to implement data transfer betweenthe artificial intelligence chip and an external equipment; and

the control device is configured to monitor a state of the artificialintelligence chip.

A17. The board card of A16, wherein

the storage device includes a plurality of groups of storage units,where each group of the storage units is connected with the artificialintelligence chip by a bus, and the storage units are DDR SDRAM;

the chip includes a DDR controller configured to control data transferand data storage of each storage unit; and

the interface apparatus is a standard PCIE interface.

With the continuous development of artificial intelligence technology,the amount of data to be processed and data dimensions are increasing.In related arts, processors usually determine the data address byobtaining the parameters of the instructions, and then read and use dataaccording to the data address, which reduces the processing efficiencyof the processors.

One aspect of the present disclosure provides a data processing method.FIG. 1c shows a flowchart of a data processing method according to anembodiment of the present disclosure. The data processing method can beapplied to a processor, where the processor may include ageneral-purpose processor (such as a CPU (central processing unit), aGPU (graphics processor)) and a dedicated processor (such as an AIprocessor, a scientific computing processor, or a digital signalprocessor, etc.). This disclosure does not limit the type of theprocessor to which the disclosed methods can be applied. As shown inFIG. 1c , the data processing method includes:

a step S11 c: when a decoded processing instruction is a descriptormanagement instruction, obtaining a management parameter of thedescriptor in the processing instruction, where the descriptor is usedto indicate a shape of tensor.

The tensor may have various forms of data structure. In someembodiments, the tensor may have different dimensions, for example, ascalar can be viewed as a 0-dimensional tensor, a vector can be viewedas a one-dimensional tensor, and a matrix can be a tensor of two or moredimensions. The shape of a tensor indicates dimensions of the tensor, asize of each dimension of the tensor, and the like.

For example, the shape of a tensor:

$\begin{bmatrix}1 & 2 & 3 & 4 \\11 & 22 & 33 & 44\end{bmatrix}$

can be described by the descriptor as (2, 4). In other words, the tensoris represented as a 2-dimensional tensor by two parameters: the firstparameter 2 corresponds to the size of a first dimension (column), andthe second parameter 4 corresponds to the size of a second dimension(row). It should be noted that the present disclosure does not limit themanner in which the descriptor indicates the shape of the tensor. Whentensor data is stored in a memory, the shape of the tensor data cannotbe determined according to a data address (or storage area) of thetensor, and then related information such as the relationship between aplurality of pieces of tensor data cannot be determined, as a result,the processor is inefficient in accessing tensor data, and the datasynchronization is also complicated. In this case, a descriptor can beintroduced to indicate the shape of the tensor.

In some embodiments, the descriptor may include an identifier, content,and the like. The identifier of the descriptor may be used todistinguish descriptors. For example, the identifier may be a serialnumber. The content of the descriptor may be used to describe the shapeof the tensor data, and the content of the descriptor may include atleast one shape parameter (such as a size of each dimension of thetensor, etc.) representing the shape of the tensor data, and may alsoinclude at least one address parameter (such as a base address of adatum point) representing an address of the tensor data. The presentdisclosure does not limit the specific parameters included in thecontent of the descriptor.

In some embodiments, a descriptor management instruction may implementregistration, modification, release, and other operations of thedescriptor, and set corresponding operation codes for the managementinstructions. For example, a descriptor can be registered (created)through a descriptor registration instruction (TRCreat); variousparameters (shape, address, etc.) of the descriptor can be modifiedthrough the descriptor modification instruction; and the descriptor canbe released (deleted) through the descriptor release instruction(TRRelease). The present disclosure does not limit the type of thedescriptor management instruction and the operation codes.

In some embodiments, the decoded processing instruction may include anoperation code and one or more operands. The operation code is used toindicate the processing type corresponding to the processinginstruction, and the operand is used to indicate the data to beprocessed. For the decoded processing instruction, the processing typeof the processing instruction can be determined according to theoperation code of the processing instruction. When the decodedprocessing instruction is a descriptor management instruction, amanagement parameter of the descriptor in the processing instruction canbe obtained, where the management parameter may be used to indicate anoperating parameter of the descriptor management instruction. Forexample, the management parameter of the descriptor registrationinstruction may include the identifier of the descriptor, the content ofthe tensor data indicated by the descriptor, etc. The present disclosuredoes not limit the specific content of the management parameter.

The data processing method further includes:

a step S12 c: executing the processing instruction according to themanagement parameter.

In other words, the processing instruction may be executed according tothe obtained management parameter. For example, when the processinginstruction is a descriptor registration instruction, after themanagement parameter of the descriptor registration instruction isobtained, the descriptor registration instruction can be executedaccording to the management parameter to create a correspondingdescriptor.

According to embodiments of the present disclosure, when the decodedprocessing instruction is a descriptor management instruction, themanagement parameter in the instruction can be obtained, and theprocessing instruction can be executed according to the managementparameter, so that the descriptor can be managed through the descriptormanagement instruction, which may improve the processing efficiency of aprocessor on a descriptor.

In some embodiments, the descriptor is used to indicate a shape ofN-dimensional tensor data, where N is an integer greater than or equalto 0, and the content of the descriptor includes at least one shapeparameter indicating the shape of the tensor data.

In some embodiments, a descriptor may be used to indicate the shape ofthe N-dimensional tensor data, where the value of N can be determinedaccording to a count of dimensions (orders) of the tensor data, and canalso be set according to the usage of the tensor data. For example, whenthe tensor data is 3-dimensional tensor data, the value of N is 3(determined according to the dimension), and the descriptor can be usedto indicate the shape (such as offset, size, etc.) of the 3-dimensionaltensor data in three dimensions. It should be understood that thoseskilled in the art can set the value of N according to actual needs,which is not limited in the present disclosure.

In some embodiments, the content of the descriptor may include at leastone shape parameter (such as a size of each dimension of the tensor,etc.) representing the shape of the tensor data. The present disclosuredoes not limit the specific parameters included in the content of thedescriptor.

In the embodiment, by using the descriptor to indicate the tensor data,the shape of the tensor data can be described, and related informationsuch as the relationship among a plurality of pieces of tensor data canbe determined accordingly, thus improving the efficiency of accessingtensor data.

In some embodiments, the identifier and content of the descriptor can bestored in the descriptor storage space, where the descriptor storagespace can be a storage space in an internal memory (such as a register,an on-chip SRAM, or other medium cache, etc.) of the control circuit.Similarly, the data storage space of the tensor data indicated by thedescriptor may also be a storage space in the internal memory (such asan on-chip cache) of the control circuit or a storage space in anexternal memory (an off-chip memory) connected to the control circuit.The data address of the data storage space may be an actual physicaladdress or a virtual address. The present disclosure does not limit aposition of the descriptor storage space and a position of the datastorage space, and the type of the data address.

In some embodiments, the identifier and content of the descriptor, andtensor data indicated by the descriptor can be stored in a same area.For example, a continuous area of an on-chip cache with addressesADDR0-ADDR1023 can be used to store the above information. Within thisarea, addresses ADDR0-ADDR31 can be used to store the identifier of thedescriptor, addresses ADDR32-ADDR63 can be used to store the content ofthe descriptor, and addresses ADDR64-ADDR1023 can be used to store thetensor data indicated by the descriptor. The address ADDR is not limitedto 1 bit or 1 byte, and is an address unit used to represent an address.Those skilled in the art can determine the storage area and the addressthereof according to the specific applications, which is not limited inthe present disclosure.

In some embodiments, the identifier and content of the descriptor, andthe tensor data indicated by the descriptor can be respectively storedin different areas of an internal memory. For example, a register can beused as a descriptor storage space to store the identifier and contentof the descriptor, and an on-chip cache can be used as a data storagespace to store the tensor data indicated by the descriptor.

In some embodiments, a special register (SR) may be provided for thedescriptor, where data in the descriptor may be an immediate number orbe obtained from the special register. When the register is used tostore the identifier and content of the descriptor, a serial number ofthe register can be used to indicate the identifier of the descriptor.For example, if the serial number of the register is 0, the identifierof a descriptor stored in the register is 0. When the descriptor in theregister is valid, an area can be allocated in a caching space (such ascreating a tensor caching unit for each piece of tensor data in thecache) according to a size of tensor data indicated by the descriptorfor storing the tensor data. It should be understood that a presetcaching space may also be used to store the tensor data, which is notlimited in the present disclosure.

In some embodiments, the identifier and content of the descriptor can bestored in an internal memory, and the tensor data indicated by thedescriptor can be stored in an external memory. For example, theidentifier and content of the descriptor may be stored on-chip and thetensor data indicated by the descriptor may be stored off-chip.

In some embodiments, the data address of the storage area correspondingto the identifier of the descriptor may be a fixed address. For example,a separate storage space may be allocated for tensor data, where a startaddress of each piece of tensor data in the data storage spacecorresponds to one identifier of the descriptor. In this case, the dataaddress of the tensor data indicated by the descriptor can be directlydetermined according to the identifier of the descriptor andcorresponding relationships.

It should be understood that those skilled in the art can set theidentifier and content of the descriptor and the specific method forstoring the tensor data indicated by the descriptor according to actualneeds, which is not limited in the present disclosure.

In some embodiments, the identifier of a descriptor, the content of thedescriptor, and the tensor data indicated by that descriptor can belocated close to each other in the memory, or can be stored in differentareas of the memory distant from each other; the identifier of adescriptor, the content of the descriptor, and the tensor data indicatedby that descriptor can be stored in an internal memory and/or anexternal memory, which makes the storage of descriptor and tensor dataconvenient and flexible, thereby improving processing the efficiency.

In some embodiments, the descriptor is also used to indicate an addressof N-dimensional tensor data, and the content of the descriptor furtherincludes at least one address parameter indicating the address of thetensor data.

In some embodiments, when the data address of the data storage spacecorresponding to the identifier of the descriptor is a variable address,the descriptor may be also used to indicate the address of N-dimensionaltensor data, where the content of the descriptor may further include atleast one address parameter representing the address of the tensor data.For example, if the tensor data is a 3-dimensional data, when thedescriptor points to the address of the tensor data, the content of thedescriptor may include an address parameter indicating the address ofthe tensor data, such as a start address of the tensor data, or thecontent of the descriptor may include a plurality of address parametersof the address of the tensor data, such as a start address+addressoffset of the tensor data, or address parameters of the tensor data ineach dimension. Those skilled in the art can set the address parametersaccording to actual needs, which is not limited in the presentdisclosure.

In some embodiments, a descriptor can be used to indicate the address oftensor data. Through the address of tensor data indicated by thedescriptor, the relationship between a plurality of tensor data can bedetermined, thereby improving the access efficiency of the processor.

In some embodiments, the address parameter of the tensor data includes abase address of the datum point of the descriptor in the data storagespace of the tensor data, where the base address may be differentaccording to the change of the datum point. The present disclosure doesnot limit the selection of the datum point.

In some embodiments, the base address may include a start address of thedata storage space. When the datum point of the descriptor is a firstdata block of the data storage space, the base address of the descriptoris the start address of the data storage space. When the datum point ofthe descriptor is other data than the first data block in the datastorage space, the base address of the descriptor is the physicaladdress of the data block in the data storage space.

In some embodiments, the address parameter of the tensor data mayinclude the base address corresponding to the datum point of thedescriptor. Different datum points can be determined according to theoperation and/or actual needs, and the corresponding base address isused in the content of the descriptor. As a result, the content of thedescriptor is more in line with operation and/or usage requirements, anddata processing efficiency can be improved.

In some embodiments, the shape parameter of a N-dimensional tensor dataincludes at least one of the followings: a size of the data storagespace of the tensor data in at least one of the N dimensions, a size ofthe storage area in at least one of the N dimensions, an offset of thestorage area in at least one of the N dimensions, a position of at leasttwo vertices at diagonal positions in the N dimensions relative to thedatum point, and a mapping relationship between a data descriptionposition of the tensor data indicated by the descriptor and the dataaddress of the tensor data indicated by the descriptor. The datadescription position is a mapping position of a point or an area in thetensor data indicated by the descriptor, for example, if the tensor datais 3-dimensional data, the descriptor can use a coordinate (x, y, z) torepresent the shape of the tensor data, and the data descriptionposition of the tensor data can be represented by the coordinate (x, y,z), and the data description position of the tensor data may be aposition of a point or an area to which the tensor data is mapped in a3-dimensional space.

It should be understood that those skilled in the art may select a shapeparameter representing tensor data according to actual conditions, whichis not limited in the present disclosure.

In some embodiments, the shape parameter of tensor data may include atleast one of size, offset, position, and mapping relationship, differentshape parameters can be used in the content of descriptor according tothe operation and/or actual needs. As a result, the content of thedescriptor is more in line with operation and/or usage requirements, anddata processing efficiency can be improved.

In some embodiments, the descriptor management instruction includes adescriptor registration instruction, and the management parameterincludes at least one of the identifier of the descriptor, the shape ofthe tensor data indicated by the descriptor, and the content of thetensor data indicated by the descriptor.

The data processing method further includes:

a step S12 c: when the processing instruction is a descriptorregistration instruction, registering a descriptor according to at leastone of the identifier of the descriptor, the shape of the tensor dataindicated by the descriptor, and the content of the tensor dataindicated by the descriptor.

In some embodiments, the descriptor registration instruction may be usedto register a descriptor, and the management parameter of the descriptorregistration instruction may include at least one of the identifier (ID)of the descriptor, the shape of the tensor, and the content of thetensor data indicated by the descriptor. For example, the managementparameter of the descriptor registration instruction may include anidentifier TR0 and the shape of the tensor data (a count of dimensions,a size of each dimension, an offset, a start data address, etc.). Thepresent disclosure does not limit the specific content of the managementparameter.

In some embodiments, a descriptor may be registered according to atleast one of the identifier of the descriptor, the shape of the tensordata indicated by the descriptor, and the content of the tensor dataindicated by the descriptor.

For example, when the management parameter of the descriptorregistration instruction includes the identifier TR0 of the descriptor,the description registration instruction can be executed according toTR0, and the descriptor TR0 can be registered, and the descriptor TR0can be stored in the descriptor storage space (such as a register)corresponding to TR0.

In some embodiments, when the management parameter of the descriptorregistration instruction includes the shape of the tensor data indicatedby the descriptor, the content of the descriptor can be determinedaccording to the shape of the tensor data indicated by the descriptor,and the content of the descriptor can be stored in the descriptorstorage space, then the process of registering the descriptor can becompleted. When the management parameter of the descriptor registrationinstruction also includes the identifier of the descriptor, after thecontent of the descriptor is determined, the content of the descriptorcan be stored in the descriptor storage space corresponding to theidentifier of the descriptor, then the process of registering thedescriptor can be completed. If the identifier of the descriptor doesnot have a corresponding descriptor storage space, the content of thedescriptor can be stored in the descriptor storage space, and thecorrespondence between the identifier of the descriptor and thedescriptor storage space can be established, then the process ofregistering the descriptor can be completed.

In some embodiments, when the management parameter of the descriptorregistration instruction includes the content of the tensor dataindicated by the descriptor, the content of the descriptor can bedetermined according to the content of the tensor data indicated by thedescriptor, and the correspondence between the content of the tensordata and the content of the descriptor can be established, and then thecontent of the descriptor can be stored in the descriptor storage space,then the process of registering the descriptor can be completed. Whenthe management parameter also includes the identifier of the descriptor,after the content of the descriptor is determined, the content of thedescriptor can be stored in the descriptor storage space correspondingto the identifier of the descriptor, then the process of registering thedescriptor can be completed. If the identifier of the descriptor doesnot have a corresponding fixed descriptor storage space, the content ofthe descriptor can be stored in the descriptor storage space, and thecorrespondence between the identifier of the descriptor and thedescriptor storage space can be established, then the process ofregistering the descriptor can be completed.

In some embodiments, the descriptor can also be registered according tothe shape of the tensor data indicated by the descriptor and the contentof the tensor data indicated by the descriptor, or the descriptor canalso be registered according to the identifier of the descriptor, theshape of the tensor data indicated by the descriptor, and the content ofthe tensor data indicated by the descriptor. The present disclosure doesnot limit the combination mode and specific value of managementparameters in the descriptor registration instruction.

In some embodiments, the descriptor registration instruction may includemanagement parameters of a plurality of descriptors. For example, whenthe descriptor registration instruction may include identifiers TR0,TR1, and TR2 of the descriptors, the TR0, TR1, and TR2 can be registeredrespectively according to management parameters (at least one of theidentifier of the descriptor, the shape of the tensor data indicated bythe descriptor, and the content of the tensor data indicated by thedescriptor). The registration process of each TR is the same or similarto the above registration process. In this way, a plurality ofdescriptors can be registered in batches according to a piece ofinstruction, which may improve the registration efficiency of thedescriptor.

In some embodiments, the descriptor can be registered according to atleast one of the identifier of the descriptor, the shape of the tensordata indicated by the descriptor, and the content of the tensor dataindicated by the descriptor, so that the registration of the descriptorcan satisfy a plurality of operations and/or actual needs, which canimprove the processing efficiency of the descriptor.

In some embodiments, the registering a descriptor according to at leastone of the identifier of the descriptor, the shape of the tensor dataindicated by the descriptor, and the content of the tensor dataindicated by the descriptor may include: determining a first storagearea for the content of the descriptor in the descriptor storage space,and a second storage area for the tensor data indicated by the contentof the descriptor in the data storage space; determining the content ofthe descriptor and establishing correspondence between the descriptorand the second storage area according to at least one of the identifierof the descriptor, the shape of the tensor data indicated by thedescriptor, and the content of the tensor data indicated by thedescriptor; and storing the content of the descriptor in the firststorage area.

In some embodiments, before the descriptor is registered, the firststorage area for the content of the descriptor in the descriptor storagespace and the second storage area for the tensor data indicated by thecontent of the descriptor in the data storage space may be determined.

For example, if at least one of the storage areas has been preset, thefirst storage area and/or the second storage area may be directlydetermined. For example, it is preset that the content of the descriptorand the content of the tensor data are stored in a same storage space,and the storage address of the content of the descriptor correspondingto the identifier TR0 of the descriptor is ADDR32-ADDR63, and thestorage address of the content of the tensor data is ADDR64-ADDR1023,then the two addresses can be directly determined as the first storagearea and the second storage area.

In some embodiments, if there is no preset storage area, the firststorage area may be allocated in the descriptor storage space for thecontent of the descriptor, and the second storage area may be allocatedin the data storage space for the content of the tensor data. Thestorage area may be allocated through the control circuit or the tensorcontrol module, which is not limited in the present disclosure.

In some embodiments, after the first storage area and the second storagearea are determined, the content of the descriptor can be determinedaccording to at least one of the identifier of the descriptor, the shapeof the tensor data indicated by the descriptor, and the content of thetensor data indicated by the descriptor, and the correspondence betweenthe descriptor and the second storage area can be established; and thenthe content of the descriptor can be stored in the first storage area,then the process of registering the descriptor can be completed.

For example, for the tensor data 23 shown in FIG. 2, the registrationparameter may include a start address PA_start (base address) of thedata storage space 21, an offset 25 (offset_x) in the X-axis direction,and an offset 24 (offset_y) in the Y-axis direction, a size in theX-axis direction (size_x), and a size in the Y-axis direction (assize_y). Based on the parameters, the content of the descriptor can berepresented as the formula

$\begin{matrix}{{X{direction}:{ori\_ x}},{offset\_ x},{size\_ x}} \\{{Y{direction}:{ori\_ y}},{offset\_ y},{size\_ y}} \\{PA\_ start}\end{matrix}$

and stored in the first storage area, thereby completing theregistration process of the descriptor.

By adopting the above-mentioned method provided by the presentdisclosure, the descriptor can be automatically created according to thedescriptor registration instruction, and the correspondence between thetensor data indicated by the descriptor and the data address can berealized, so that the data address can be obtained through the contentof the descriptor during data processing, and the data access efficiencyof the processor can be improved.

In some embodiments, the content of the tensor data indicated by thedescriptor includes at least one of immediate data and data in aregister.

In some embodiments, the content of the tensor data indicated by thedescriptor may include immediate data, where the immediate data maybetensor data that does not change during data processing. After thecorrespondence between the descriptor and the immediate data isestablished, the immediate data can be replaced by the descriptor duringthe data processing process. The content of the tensor data indicated bythe descriptor may also include the data in the register. After thecorrespondence between the descriptor and the data in the register isestablished, a serial number of the register may be taken as anidentifier of the descriptor.

In some embodiments, using a descriptor to indicate immediate data anddata in a register may reduce the complexity of using the immediate dataand the data in the register, thereby improving the efficiency of dataprocessing.

In some embodiments, a descriptor management instruction may include afirst descriptor release instruction, and the management parameterincludes an identifier of the descriptor. The step S12 c may include:when the processing instruction is a first descriptor releaseinstruction, releasing the descriptor corresponding to the identifieraccording to the identifier of the descriptor. The first descriptorrelease instruction may be used to release (delete) a descriptor, andthe management parameter of the first descriptor release instruction mayinclude the identifier of the descriptor, where the identifier of thedescriptor may be used to indicate the released descriptor.

In some embodiments, the management parameter of the first descriptorrelease instruction may include an identifier of at least onedescriptor, in other words, the first descriptor release instruction mayrelease one descriptor or simultaneously release a plurality ofdescriptors.

In some embodiments, the first descriptor release instruction mayinclude identifiers of part of the descriptors, in other words, onlypart of the descriptors among current descriptors may be released, orthe first descriptor release instruction may include identifiers of alldescriptors, in other words, all the current descriptors may bereleased.

In some embodiments, when the processing instruction is the firstdescriptor release instruction, the descriptor corresponding to theidentifier may be released according to the identifier of thedescriptor. For example, when the management parameters of the firstdescriptor release instruction are TR2 and TR3, the first descriptorrelease instruction may release descriptors corresponding to TR2 and TR3according to TR2 and TR3.

In some embodiments, descriptors can be released according to theidentifiers of the descriptors. Part or all of the descriptors, and oneor more descriptors can be released at the same time according to actualneeds, so that the release mode of the descriptors can meet variousprocessing requirements, thereby improving the release efficiency of thedescriptors.

In some embodiments, the releasing the descriptor corresponding to theidentifier according to the identifier of the descriptor may include:releasing the storage area of the descriptor in the descriptor storagespace and the storage area of the content of the tensor data indicatedby the descriptor in the data storage space, respectively.

In other words, when the descriptor corresponding to the identifier isreleased according to the identifier of the descriptor, the storage areaoccupied by the descriptor may be released at the same time. In otherwords, the storage area of the descriptor in the descriptor storagespace and the storage area of the content of the tensor data indicatedby the descriptor in the data storage space may be respectivelyreleased. In this way, the space occupied by the descriptor may bereleased after the descriptor is used, so that the limited storageresources can be reused, and the resource utilization efficiency isimproved.

In some embodiments, the descriptor management instruction includes asecond descriptor release instruction, and the management parameterincludes an identifier of the descriptor, where the step S12 c mayinclude: when the processing instruction is a second descriptor releaseinstruction, storing the content of the descriptor stored in thedescriptor storage space in a designated storage space according to theidentifier of the descriptor, and releasing the descriptor correspondingto the identifier. The second descriptor release instruction may be usedto release (delete) a descriptor, and the management parameter of thesecond descriptor release instruction may include the identifier of thedescriptor, where the identifier of the descriptor may be used toindicate the released descriptor.

In some embodiments, when the processing instruction is a seconddescriptor release instruction, according to the identifier of thedescriptor, the content of the descriptor stored in the descriptorstorage space is first stored in a designated storage space, and thenthe descriptor corresponding to the identifier of the descriptor isregistered. In other words, the second descriptor release instructionmay perform the release operation after the content of the releaseddescriptor is stored. By storing the content of the descriptor first andthen releasing the descriptor, the resources (such as the identifier ofthe descriptor, the storage space, etc.) occupied by the currentdescriptor may be released while the content of the descriptor that needto be used later is stored, thereby improving the resource utilizationefficiency.

In some embodiments, the descriptor management instruction includes adescriptor modification instruction, and the management parameterincludes at least one of the identifier of the descriptor, the contentof the modified descriptor, and the content of the tensor data indicatedby the descriptor. The step S12 c may include: when the processinginstruction is a descriptor modification instruction, determining theupdated content of the descriptor according to the management parameterof the descriptor; according to the updated content, updating at leastone of the identifier of the descriptor, the content of the descriptorin the descriptor storage space, and the content of tensor data in thedata storage space.

For example, the descriptor modification instruction can be used tomodify various parameters of the descriptor, such as the identifier ofthe descriptor, the shape of the tensor, and the like. The managementparameters of the descriptor modification instruction may include atleast one of the identifier of the descriptor, the content of themodified descriptor, and the content of the tensor data indicated by thedescriptor. The present disclosure does not limit the specific contentof the management parameters of the descriptor modification instruction.

In some embodiments, when the processing instruction is a descriptormodification instruction, the content of the updated descriptor can bedetermined according to the management parameters of the descriptor, forexample, the dimension of the tensor may be changed from 3 dimensions to2 dimensions, the size of the tensor data in one or more dimensionaldirections may be changed.

In some embodiments, after the content to be updated is determined, atleast one of the identifier of the descriptor in the descriptor storagespace, the content of the descriptor in the descriptor storage space,and the content of tensor data in the data storage space can be updatedto modify the tensor data, so that the updated descriptor can indicatethe modified tensor data. The present disclosure does not limit thescope of the content to be updated and the specific updating method.

By adopting the above-mentioned method provided by the presentdisclosure, when the tensor data indicated by the descriptor changes,the descriptor is directly modified to maintain the correspondencebetween the descriptor and the tensor data, which improves theefficiency of resource utilization.

It should be noted that although the above-mentioned embodiment is usedas an example to introduce the data processing method as describedabove, those skilled in the art can understand that the presentdisclosure should not be limited thereto. In fact, users can flexiblyset each step according to personal preference and/or actual applicationscenarios, as long as it conforms to the technical solution of thepresent disclosure.

FIG. 3a shows a block diagram of a data processing apparatus accordingto an embodiment of the present disclosure. As shown in FIG. 3a , thedata processing apparatus includes: a parameter obtaining module 31 aconfigured to obtain, when a decoded processing instruction is adescriptor management instruction, a management parameter of thedescriptor in the processing instruction, where the descriptor is usedto indicate a shape of tensor; and

an instruction executing module 32 a configured to execute theprocessing instruction according to the management parameter.

In some embodiments, the descriptor management instruction includes adescriptor registration instruction, and the management parameterincludes at least one of the identifier of the descriptor, the shape ofthe tensor data indicated by the descriptor, and the content of thetensor data indicated by the descriptor.

The instruction executing module 32 a includes:

a register sub-module configured to register, when the processinginstruction is a descriptor registration instruction, a descriptoraccording to at least one of the identifier of the descriptor, the shapeof the tensor data indicated by the descriptor, and the content of thetensor data indicated by the descriptor.

In some embodiments, the register sub-module is further configured to:

determine a first storage area for the content of the descriptor in thedescriptor storage space, and a second storage area for the tensor dataindicated by the content of the descriptor in the data storage space;

determine the content of the descriptor and establish correspondencebetween the descriptor and the second storage area according to at leastone of the identifier of the descriptor, the shape of the tensor dataindicated by the descriptor, and the content of the tensor dataindicated by the descriptor; and

store the content of the descriptor in the first storage area.

In some embodiments, the content of the tensor data indicated by thedescriptor includes at least one kind of immediate data or data in aregister.

In some embodiments, the instruction executing module 32 a includes:

a first release sub-module configured to release, when the processinginstruction is a first descriptor release instruction, the descriptorcorresponding to the identifier according to the identifier of thedescriptor.

In some embodiments, the first release sub-module is further configuredto: release the storage area of the descriptor in the descriptor storagespace and the storage area of the content of the tensor data indicatedby the descriptor in the data storage space, respectively.

In some embodiments, the descriptor management instruction includes asecond descriptor release instruction, and the management parameterincludes an identifier of the descriptor. The instruction executingmodule 32 a includes:

a storage space determining sub-module configured to store, when theprocessing instruction is a second descriptor release instruction, thecontent of the descriptor stored in the descriptor storage space in adesignated storage space according to the identifier of the descriptor;and

a second release sub-module configured to release the descriptorcorresponding to the identifier.

In some embodiments, the descriptor management instruction includes adescriptor modification instruction, and the management parameterincludes at least one of the identifier of the descriptor, the contentof the modified descriptor, and the content of the tensor data indicatedby the descriptor.

The instruction executing module 32 a includes:

an updated content determining sub-module configured to determine, whenthe processing instruction is a descriptor modification instruction,content to be updated of the descriptor according to the managementparameter of the descriptor; and

a modifying sub-module configured to update, according to the content tobe updated, at least one of the identifier of the descriptor, thecontent of the descriptor in the descriptor storage space, and thecontent of tensor data in the data storage space.

In some embodiments, the descriptor is used to indicate a shape ofN-dimensional tensor data, where N is an integer greater than or equalto 0, and the content of the descriptor includes at least one shapeparameter indicating the shape of the tensor data.

In some embodiments, the descriptor is also used to indicate an addressof N-dimensional tensor data, and the content of the descriptor furtherincludes at least one address parameter indicating the address of thetensor data.

In some embodiments, the address parameter of the tensor data includes abase address of a datum point of the descriptor in the data storagespace of the tensor data.

In some embodiments, the shape parameter of the tensor data includes atleast one of following: a size of the data storage space in at least oneof N dimensions, a size of the storage area in at least one of Ndimensions, an offset of the storage area in at least one of Ndimensions, a position of at least two vertices at diagonal positions inN dimensions relative to the datum point, and a mapping relationshipbetween a data description position of the tensor data indicated by thedescriptor and the data address of the tensor data indicated by thedescriptor.

In some embodiments, the present disclosure provides an artificialintelligence chip including the above-mentioned data processingapparatus.

In some embodiments, the present disclosure further provides a boardcard including: a storage device, an interface apparatus, a controldevice, and the above-mentioned artificial intelligence chip. Theartificial intelligence chip is connected to the storage device, thecontrol device, and the interface apparatus respectively; the storagedevice is configured to store data; the interface apparatus isconfigured to implement data transfer between the artificialintelligence chip and an external equipment; and the control device isconfigured to monitor a state of the artificial intelligence chip.

In some embodiments, the storage device includes a plurality of groupsof storage units, where each group of the storage units is connectedwith the artificial intelligence chip by a bus, and the storage unitsare DDR SDRAMs. The chip includes a DDR controller configured to controldata transfer and data storage of each storage unit. The interfaceapparatus is a standard PCIE interface.

A1. A data processing method, comprising:

when a decoded processing instruction is a descriptor managementinstruction, obtaining a management parameter of the descriptor in theprocessing instruction, wherein the descriptor is used to indicate ashape of tensor; and

executing the processing instruction according to the managementparameter.

A2. The data processing method of A1, wherein the descriptor managementinstruction includes a descriptor registration instruction, and themanagement parameter includes at least one of an identifier of thedescriptor, a shape of tensor data indicated by the descriptor, andcontent of the tensor data indicated by the descriptor, wherein theexecuting the processing instruction according to the managementparameter includes:

when the processing instruction is a descriptor registrationinstruction, registering a descriptor according to at least one of theidentifier of the descriptor, the shape of the tensor data indicated bythe descriptor, and the content of the tensor data indicated by thedescriptor.

A3. The data processing method of A2, wherein the registering adescriptor according to at least one of the identifier of thedescriptor, the shape of the tensor data indicated by the descriptor,and the content of the tensor data indicated by the descriptor includes:

determining a first storage area for the content of the descriptor inthe descriptor storage space, and a second storage area for the tensordata indicated by the content of the descriptor in the data storagespace;

determining the content of the descriptor and establishingcorrespondence between the descriptor and the second storage areaaccording to at least one of the identifier of the descriptor, the shapeof the tensor data indicated by the descriptor, and the content of thetensor data indicated by the descriptor; and

storing the content of the descriptor in the first storage area.

A4. The data processing method of A2 or A3, wherein the content of thetensor data indicated by the descriptor includes at least one kind ofimmediate data or data in a register.

A5. The data processing method of A1, wherein the descriptor managementinstruction includes a first descriptor release instruction, and themanagement parameter includes an identifier of the descriptor, whereinthe executing the processing instruction according to the managementparameter includes:

when the processing instruction is a first descriptor releaseinstruction, releasing the descriptor corresponding to the identifieraccording to the identifier of the descriptor.

A6. The data processing method of A5, wherein the releasing thedescriptor corresponding to the identifier according to the identifierof the descriptor includes:

releasing a storage area of the descriptor in the descriptor storagespace and a storage area of the content of the tensor data indicated bythe descriptor in the data storage space, respectively.

A7. The data processing method of A1, wherein the descriptor managementinstruction includes a second descriptor release instruction, and themanagement parameter includes an identifier of the descriptor, whereinthe executing the processing instruction according to the managementparameter includes:

when the processing instruction is a second descriptor releaseinstruction, storing the content of the descriptor stored in thedescriptor storage space in a designated storage space according to theidentifier of the descriptor; and

releasing the descriptor corresponding to the identifier.

A8. The data processing method of A1, wherein the descriptor managementinstruction includes a descriptor modification instruction, and themanagement parameter includes at least one of the identifier of thedescriptor, the content of the modified descriptor, and the content ofthe tensor data indicated by the descriptor, wherein the executing theprocessing instruction according to the management parameter includes:

when the processing instruction is a descriptor modificationinstruction, determining content to be updated of the descriptoraccording to the management parameter of the descriptor; and

according to the content to be updated, updating at least one of theidentifier of the descriptor, the content of the descriptor in thedescriptor storage space, and the content of tensor data in the datastorage space.

A9. The data processing method of any one of A1-A8, wherein thedescriptor is used to indicate a shape of N-dimensional tensor data,wherein N is an integer greater than or equal to 0, and the content ofthe descriptor includes at least one shape parameter indicating theshape of the tensor data.

A10. The data processing method of A9, wherein the descriptor is alsoused to indicate an address of N-dimensional tensor data, and thecontent of the descriptor further includes at least one addressparameter indicating the address of the tensor data.

A11. The data processing method of A10, wherein the address parameter ofthe tensor data includes a base address of a datum point of thedescriptor in the data storage space of the tensor data.

A12. The data processing method of A11, wherein the shape parameter ofthe tensor data includes at least one of following:

a size of the data storage space in at least one of N dimensions, a sizeof the storage area in at least one of N dimensions, an offset of thestorage area in at least one of N dimensions, a position of at least twovertices at diagonal positions in N dimensions relative to the datumpoint, and a mapping relationship between a data description position ofthe tensor data indicated by the descriptor and the data address of thetensor data indicated by the descriptor.

A13. A data processing apparatus, comprising:

a parameter obtaining module configured to obtain, when a decodedprocessing instruction is a descriptor management instruction, amanagement parameter of the descriptor in the processing instruction,wherein the descriptor is used to indicate a shape of tensor; and

an instruction executing module configured to execute the processinginstruction according to the management parameter.

A14. The data processing apparatus of A13, wherein the descriptormanagement instruction includes a descriptor registration instruction,and the management parameter includes at least one of an identifier ofthe descriptor, a shape of tensor data indicated by the descriptor, andcontent of the tensor data indicated by the descriptor, wherein theinstruction executing module includes:

a register sub-module configured to register, when the processinginstruction is a descriptor registration instruction, a descriptoraccording to at least one of the identifier of the descriptor, the shapeof the tensor data indicated by the descriptor, and the content of thetensor data indicated by the descriptor.

A15. The data processing apparatus of A14, wherein the registersub-module is further configured to:

determine a first storage area for the content of the descriptor in adescriptor storage space, and a second storage area for the tensor dataindicated by the content of the descriptor in a data storage space;

determine the content of the descriptor and establish correspondencebetween the descriptor and the second storage area according to at leastone of the identifier of the descriptor, the shape of the tensor dataindicated by the descriptor, and the content of the tensor dataindicated by the descriptor; and

store the content of the descriptor in the first storage area.

A16. The data processing apparatus of A14 or A15, wherein the content ofthe tensor data indicated by the descriptor includes at least one kindof immediate data or data in a register.

A17. The data processing apparatus of A13, wherein the descriptormanagement instruction includes a first descriptor release instruction,and the management parameter includes the identifier of the descriptor,wherein the instruction executing module includes:

a first release sub-module configured to release, when the processinginstruction is a first descriptor release instruction, the descriptorcorresponding to the identifier according to the identifier of thedescriptor.

A18. The data processing apparatus of A17, wherein the first releasesub-module is further configured to:

release a storage area of the descriptor in the descriptor storage spaceand a storage area of the content of the tensor data indicated by thedescriptor in the data storage space, respectively.

A19. The data processing apparatus of A13, wherein the descriptormanagement instruction includes a second descriptor release instruction,and the management parameter includes the identifier of the descriptor,wherein the instruction executing module includes:

a storage space determining sub-module configured to store, when theprocessing instruction is a second descriptor release instruction, thecontent of the descriptor stored in the descriptor storage space in adesignated storage space according to the identifier of the descriptor;and

a second release sub-module configured to release the descriptorcorresponding to the identifier.

A20. The data processing apparatus of A13, wherein the descriptormanagement instruction includes a descriptor modification instruction,and the management parameter includes at least one of the identifier ofthe descriptor, the content of the modified descriptor, and the contentof the tensor data indicated by the descriptor, wherein the instructionexecuting module includes:

an updated content determining sub-module configured to determine, whenthe processing instruction is a descriptor modification instruction,content to be updated of the descriptor according to the managementparameter of the descriptor; and

a modifying sub-module configured to update, according to the content tobe updated, at least one of the identifier of the descriptor, thecontent of the descriptor in the descriptor storage space, and thecontent of tensor data in the data storage space.

A21. The data processing apparatus of any one of A13-A20, wherein thedescriptor is used to indicate a shape of N-dimensional tensor data,wherein N is an integer greater than or equal to 0, and the content ofthe descriptor includes at least one shape parameter indicating theshape of the tensor data.

A22. The data processing apparatus of A21, wherein the descriptor isalso used to indicate an address of N-dimensional tensor data, and thecontent of the descriptor further includes at least one addressparameter indicating the address of the tensor data.

A23. The data processing apparatus of A22, wherein the address parameterof the tensor data includes a base address of a datum point of thedescriptor in the data storage space of the tensor data.

A24. The data processing apparatus of A23, wherein the shape parameterof the tensor data includes at least one of following:

a size of the data storage space in at least one of N dimensions, a sizeof the storage area in at least one of N dimensions, an offset of thestorage area in at least one of N dimensions, a position of at least twovertices at diagonal positions in N dimensions relative to the datumpoint, and a mapping relationship between a data description position ofthe tensor data indicated by the descriptor and the data address of thetensor data indicated by the descriptor.

A25. An artificial intelligence chip comprising the data processingapparatus of any one of A13-A24.

A26. An electronic device comprising the artificial intelligence chip ofA25.

A27. A board card comprising a storage device, an interface apparatus, acontrol device, and the artificial intelligence chip of A25, wherein

the artificial intelligence chip is connected to the storage device, thecontrol device, and the interface apparatus, respectively;

the storage device is configured to store data;

the interface apparatus is configured to implement data transfer betweenthe artificial intelligence chip and an external equipment; and

the control device is configured to monitor a state of the artificialintelligence chip.

A28. The board card of A27, wherein

the storage device includes a plurality of groups of storage units,where each group of the storage units is connected with the artificialintelligence chip by a bus, and the storage units are DDR SDRAMs;

the chip includes a DDR controller configured to control data transferand data storage of each storage unit; and

the interface apparatus is a standard PCIE interface.

With the continuous development of the A1 (Artificial Intelligence)technology, it has gradually obtained wide application and worked wellin the fields of image recognition, speech recognition, and naturallanguage processing, and the like. However, as the complexity of A1algorithms is growing, the amount of data and data dimensions that needto be processed are increasing, therefore, multi-core and/or multi-chipdata are usually required for data processing. When data is synchronizedbetween cores or chips, a synchronization method adopting therelated-art may result in large synchronization overhead and lowprocessing efficiency.

In some embodiments, the present disclosure provides a datasynchronization method.

The data synchronization method provided by the present disclosure maybe applied to any one processor of a processing system (for example, anartificial intelligence chip) including a plurality of processors(multi-core). The processor may be a general-purpose processor, such asa CPU (Central Processing Unit), or an IPU (Artificial IntelligenceProcessor) for performing artificial intelligence operations. Artificialintelligence operations may include machine learning operations,brain-like operations, and the like, where machine learning operationsinclude neural network operations, k-means operations, support vectormachine operations, and the like. The artificial intelligence processormay include, for example, one or a combination of GPU (GraphicsProcessing Unit), NPU (neural-network Processing Unit), DSP (DigitalSignal Process), and Field-Programmable Gate Array (FPGA). The presentdisclosure does not limit the specific types of processors. In addition,the types of a plurality of processors in the processing system may bethe same or different, which is not limited in the present disclosure.

In some embodiments, the processor mentioned in the present disclosuremay include a plurality of processing units, and each processing unitcan independently run all assigned tasks, such as convolution, pooling,or full connection. The present disclosure does not limit the processingunit and the tasks run by the processing unit.

FIG. 1d 1 shows a schematic diagram of a processing system of a datasynchronization method according to an embodiment of the presentdisclosure. As shown in FIG. 1d 1, a processing system 100 d includes aplurality of processors 101 and a memory 102, where the plurality ofprocessors 101 are used to execute instruction sequences, and the memory102 is used to store data, which may include a RAM (Random AccessMemory) and a register file. The plurality of processors 101 in theprocessing system 100 d can share part of a storage space, such as theRAM storage space and the register file, and can also have their ownstorage space at the same time.

FIG. 1d 2 shows a flowchart of a data synchronization method accordingto an embodiment of the present disclosure. As shown in FIG. 1d 2, thedata synchronization method is applied to a first processor (anyprocessor in the processing system), and the data synchronization methodincludes:

a step S11 d: determining synchronization information of tensor dataaccording to a descriptor of the tensor data to be synchronized, wherethe descriptor is used to indicate a shape of the tensor data to besynchronized;

a step S12 d: generating a synchronization instruction according to thesynchronization information of the tensor data; and

a step S13 d: sending the synchronization instruction to a secondprocessor, where the synchronization instruction is used to instruct thesecond processor to obtain the tensor data to be synchronized accordingto the synchronization instruction.

For example, the tensor data to be synchronized may includeN-dimensional tensor data (N is an integer greater than or equal to 0,for example, N=1, 2, or 3), where the tensor may have various forms ofdata composition, and the tensor may have different dimensions, forexample, a scalar can be viewed as a 0-dimensional tensor, a vector canbe viewed as a 1-dimensional tensor, and a matrix can be a tensor of twoor more dimensions. The shape of a tensor includes dimensions of thetensor and a size of each dimension of each tensor and the like.

In this case, a descriptor (tensor descriptor) may be set to indicatethe shape of the tensor (N-dimensional tensor data), where the value ofN can be determined according to a count of dimensions (orders) of thetensor data, and can also be set according to the usage of the tensordata. For example, when the value of N is 3, the tensor data is3-dimensional tensor data, and the descriptor can be used to indicatethe shape (such as offset, size, etc.) of the 3-dimensional tensor datain three dimensions. It should be understood that those skilled in theart can set the value of N according to actual needs, which is notlimited in the present disclosure.

In some embodiments, the descriptor may include an identifier, content,and the like. The identifier of the descriptor may be used todistinguish descriptors, for example, the identifier may be a serialnumber. The content of the descriptor may include at least one shapeparameter (such as a size of each dimension of the tensor, etc.)representing the shape of the tensor data, and may also include at leastone address parameter (such as a base address of a datum point)representing an address of the tensor data. The present disclosure doesnot limit the specific parameters included in the content of thedescriptor.

By using the descriptor to indicate tensor data, the shape of tensordata can be represented, and related information such as therelationship among a plurality of pieces of tensor data can bedetermined, so as to improve the access efficiency of tensor data andreduce the complexity of data synchronization.

In some embodiments, during data processing, data synchronizationbetween a plurality of processors (such as a plurality of cores of anartificial intelligence chip) may be executed, for example, an operationresult of a processor A1 may be synchronized to a processor A2 as inputdata of another operation. In this case, a data synchronizationmechanism based on the descriptor can be used to achieve datasynchronization.

In some embodiments, the first processor is a sender for datasynchronization, and the second processor is a receiver for datasynchronization. When there is tensor data to be synchronized, in thestep S11 d, the first processor may determine the synchronizationinformation of the tensor data (for example, an identifier, shape,source, storage address, and other information of tensor data); in thestep S12 d, the first processor may generate the synchronizationinstruction according to the synchronization information; and in thestep S13 d, the first processor may send the synchronization instructionto the second processor to be synchronized. The second processor mayinclude a general-purpose processor (such as a CPU (central processingunit), a GPU (graphics processor)) and a dedicated processor (such as anAI processor, a scientific computing processor, or a digital signalprocessor, etc.). The type of the second processor may be the same as ordifferent from the type of the first processor, and this disclosure doesnot limit the type of the second processor.

In some embodiments, the first processor can actively execute datasynchronization on the second processor. For example, when the firstprocessor completes an operation and obtains an operation result (tensordata), the first processor actively synchronizes data with the secondprocessor that needs to use the operation result. In another example,the first processor may also synchronize data with the second processorin response to a synchronization request of the second processor, forexample, when receiving a synchronization request instruction from thesecond processor, the first processor starts executing datasynchronization on the second processor. The present disclosure does notlimit the timing of the start of data synchronization.

In some embodiments, when the first processor determines that there istensor data to be synchronized, the first processor may obtain thedescriptor of the tensor data. The descriptor may be a registered(created) descriptor indicating the shape of the tensor data, or a newdescriptor registered (created) according to the shape parameter of thetensor data, which is not limited in the present disclosure.

In some embodiments, according to the descriptor of the tensor data, thesynchronization information of the tensor data can be determined. Thesynchronization information may include at least one of the identifier(for example, a serial number of data), shape, source, and storageaddress of the tensor data. According to the synchronization informationof the tensor data, the synchronization instruction can be generated. Ifthe second processor already has information (for example, a descriptorindicating the tensor data to be synchronized has been registered) ofthe tensor data, the synchronization instruction may only include partof the synchronization information, such as the identifier of the tensordata, and then the synchronization instruction may instruct the secondprocessor to synchronize the tensor data according to the identifier ofthe tensor data; if the second processor does not have information ofthe tensor data, the synchronization instruction can include moresynchronization information such as the identifier and the storageaddress, and then the synchronization instruction may instruct thesecond processor to synchronize the tensor data according to thecorresponding information. The present disclosure does not limit thespecific content included in the synchronization instruction.

In some embodiments, after the synchronization instruction is generated,the synchronization instruction may be sent to the second processor toinstruct the second processor to obtain the tensor data to besynchronized according to the synchronization instruction. If thesynchronization instruction includes the identifier of the tensor data,the second processor may determine the tensor data to be synchronizedaccording to the identifier, and register or obtain the descriptorindicating the tensor data to be synchronized, and then obtain thetensor data indicated by the descriptor according to the content of thedescriptor, thereby synchronizing the tensor data. If thesynchronization instruction includes more synchronization informationsuch as the identifier and storage address, the second processor canregister the descriptor indicating the tensor data to be synchronizedaccording to the synchronization information in the instruction, andobtain the tensor data indicated by the descriptor directly according tothe content of the descriptor, thereby synchronizing the tensor data.

According to the data synchronization method provided in the embodimentof the present disclosure, by setting a descriptor indicating the shapeof tensor data, the synchronization information of the tensor data maybe determined according to the descriptor, and a synchronizationinstruction can be generated according to the synchronizationinformation and sent to the second processor to instruct the secondprocessor to obtain the tensor data to be synchronized according to thesynchronization instruction, thereby reducing synchronization overheadwithout changing a structure of the synchronization instruction, andimproving the efficiency of data synchronization.

In some embodiments, the synchronization information may include thestorage address of the tensor data to be synchronized. The step S12 dmay include: when the storage address of the tensor data to besynchronized is in a shared storage space, generating thesynchronization instruction according to the storage address of thetensor data to be synchronized to instruct the second processor toobtain the tensor data to be synchronized from the shared storage space.

For example, a plurality of processors (cores) may have a shared storagespace, for example, an off-chip memory can be accessed by both the firstprocessor and the second processor. The shared storage space may be astorage space in which a plurality of cores (processors) can accessdata, or a storage space in which some cores (processors) can accessdata. The shared storage space for cores may be preset, and the presentdisclosure does not limit the specific method for setting the sharedstorage space.

In some embodiments, the storage address of the tensor data to besynchronized can be determined according to the content of thedescriptor of the tensor data to be synchronized. If the storage addressof the tensor data to be synchronized is in the shared storage space,the second processor can also access data from the shared storage space,and then the second processor can directly read the tensor dataaccording to the storage address of the tensor data to achievesynchronization. In this case, the synchronization instruction mayinclude the storage address of the tensor data to be synchronized, inother words, the synchronization instruction may be generated accordingto the storage address of the tensor data to be synchronized. Afterreceiving the synchronization instruction, the second processor mayparse the instruction to obtain the storage address of the tensor data;according to the storage address of the tensor data, the secondprocessor may register (create) the descriptor of the tensor data to besynchronized, so that the content of the descriptor corresponds to thedata address of the tensor data, and the tensor data to be synchronizedcan be obtained from the shared storage space, thereby achieving theentire synchronization process.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, unnecessary data transfer can be avoided, theamount of transmitted data can be reduced, and the synchronizationefficiency can be improved.

In some embodiments, the synchronization information may include thestorage address of the tensor data to be synchronized. The step S12 dmay include: when the storage address of the tensor data to besynchronized is in a non-shared storage space, storing the tensor datato be synchronized in the shared storage space; and according to theaddress of the tensor data to be synchronized in the shared storagespace, generating the synchronization instruction to instruct the secondprocessor to obtain the tensor data to be synchronized from the sharedstorage space.

For example, the first processor may have a non-shared storage space inwhich the first processor may access data, and the second processorcannot access the non-shared storage space of the first processor, sothat the second processor cannot access data in the non-shared storagespace. If the storage address of the tensor data to be synchronized isin the non-shared storage space, the second processor cannot directlyobtain the tensor data. In this case, the first processor may transferthe tensor data to be synchronized and store the tensor data to besynchronized to the shared storage space, so that the second processorcan access the tensor data. After the tensor data to be synchronized istransferred and stored into the shared storage space, if a descriptorindicating the tensor data to be synchronized is not registered in thefirst processor, or a descriptor indicating the tensor data in thenon-shared storage space is registered and the descriptor cannot bemodified (for example, the descriptor is being operated), the firstprocessor may generate a descriptor of the tensor data to besynchronized, in other words, the first processor may register a newdescriptor to indicate the tensor data in the shared storage space.

In some embodiments, the first processor may generate thesynchronization instruction according to the address of the tensor datato be synchronized in the shared storage space. After receiving thesynchronization instruction, the second processor may parse theinstruction to obtain the storage address of the tensor data to besynchronized; according to the storage address of the tensor data, thesecond processor may register (create) the descriptor of the tensor datato be synchronized, so that the content of the descriptor corresponds tothe data address of the tensor data, and the second processor may obtainthe tensor data to be synchronized from the shared storage space, thenthe entire synchronization process can be completed.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the tensor data to be synchronized in thenon-shared storage space can be actively transferred and stored into theshared storage space, so that the second processor can obtain the tensordata to be synchronized, thereby reducing the amount of data transmittedbetween processors during synchronization, and improving thesynchronization efficiency.

In some embodiments, the data synchronization method further includes:determining the descriptor of the tensor data to be synchronizedaccording to the synchronization request instruction from the secondprocessor.

For example, the first processor may execute data synchronization on thesecond processor in response to the synchronization request of thesecond processor. The synchronization request instruction received fromthe second processor may include information of the tensor data to besynchronized, such as data characteristics of the tensor data to besynchronized, and the like, where the data characteristics of the tensordata may include information such as the shape, source, and address ofthe tensor data. The present disclosure does not limit the specificcontent of the synchronization request instruction. According to theinformation in the synchronization request instruction, the firstprocessor may determine the descriptor of the tensor data to besynchronized, and determine the synchronization information of thetensor data according to the descriptor, and then generate thesynchronization instruction.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the descriptor of the tensor data to besynchronized can be determined according to the synchronization requestof the second processor to generate the synchronization instruction,thereby avoiding unnecessary data synchronization and improving theefficiency of data synchronization.

In some embodiments, the synchronization request instruction includesthe data characteristics of the tensor data to be synchronized. The stepof determining the descriptor of the tensor data to be synchronizedaccording to the synchronization request instruction from the secondprocessor may include:

parsing the synchronization request instruction to obtain the datacharacteristics of the tensor data to be synchronized; and

determining the descriptor of the tensor data to be synchronizedaccording to the data characteristics of the tensor data to besynchronized.

For example, if both the first processor and the second processor haveinformation (data characteristics) of the tensor data to besynchronized, and the information is the same or has a correspondence,the synchronization request instruction may include the datacharacteristics, such as the identifier of the tensor data. The firstprocessor may parse the synchronization request instruction from thesecond processor to obtain the data characteristics of the tensor datato be synchronized.

In some embodiments, the data characteristics of the tensor data to besynchronized may include information such as the shape, source, andaddress of the tensor data. For example, the tensor data may be from aK-th sender (a K-th processor), the tensor data may be from an operationresult of a convolution operation numbered 200, the address of thetensor data may be a specific address area (for example, the addressADDR0-ADDR127), and the shape of the tensor data may be a specifiedshape (for example, the tensor data may be a 20*10 two-dimensionaltensor). Those skilled in the art can set the data characteristics ofthe tensor data to be synchronized according to the actual situation,which is not limited in the present disclosure.

In some embodiments, according to the data characteristics, the firstprocessor may determine the tensor data to be synchronized, anddetermine the descriptor of the tensor data to be synchronized, forexample, the first processor may directly obtain a descriptor orregister a corresponding descriptor. According to the descriptor of thetensor data to be synchronized, the synchronization information of thetensor data may be determined, and then the synchronization instructionmay be generated and sent to instruct the second processor tosynchronize the tensor data.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the descriptor of the tensor data to besynchronized can be determined according to the data characteristics inthe request instruction, so as to achieve the synchronization of thetensor data. In this way, there is no need to transfer tensor dataitself during synchronization, which reduces the amount of transferreddata and synchronization overhead, and improves processing efficiency.

FIG. 3b 1 shows a flowchart of a data synchronization method accordingto an embodiment of the present disclosure. The data synchronizationmethod may be applied to the second processor. As shown in FIG. 3b 1,the data synchronization method may include:

a step S21 b: parsing a synchronization instruction from a firstprocessor to obtain synchronization information of tensor data to besynchronized;

a step S22 b: determining a descriptor of the tensor data to besynchronized according to the synchronization information of the tensordata to be synchronized, where the descriptor is used to indicate ashape of the tensor data to be synchronized; and

a step S23 b: obtaining the tensor data to be synchronized according tothe descriptor of the tensor data to be synchronized.

For example, the first processor (sender) can actively execute datasynchronization on a second processor (receiver). For example, when thefirst processor completes an operation and obtains an operation result(tensor data), the first processor actively executes datasynchronization on the second processor that needs to use the operationresult.

In some embodiments, when receiving the synchronization instruction fromthe first processor, the second processor may parse the synchronizationinstruction to obtain the synchronization information (such as anidentifier, shape, and storage address of the tensor data) of the tensordata to be synchronized.

In some embodiments, if the synchronization instruction includes onlythe identifier of the descriptor, the second processor may internallysearch for the tensor data corresponding to the identifier of the tensordata and/or the descriptor corresponding to the tensor data, and thenobtain the tensor data to be synchronized according to the content ofthe descriptor, thereby achieving synchronization of the tensor data.

In some embodiments, if the synchronization instruction includes theshape and storage address of the tensor data, the second processor mayregister a descriptor indicating the tensor data to be synchronizedaccording to the shape and storage address of the tensor data, andobtain the tensor data to be synchronized according to the content ofthe descriptor, so as to realize the synchronization of the tensor data.

According to the data synchronization method provided by the presentdisclosure, by setting a descriptor indicating the shape of the tensordata, the descriptor of the tensor data may be determined according tothe synchronization information of the tensor data to be synchronized inthe synchronization instruction, and then the tensor data may beobtained, finally the synchronization of the tensor data may becompleted, which could reduce the synchronization overhead and thecomplexity of data synchronization, and improve the efficiency of datasynchronization.

In some embodiments, the synchronization information includes thestorage address of the tensor data to be synchronized. The datasynchronization method further includes:

a step S22 b: determining the identifier of the descriptor of the tensordata to be synchronized and/or the content of the descriptor accordingto the storage address of the tensor data to be synchronized; and

a step S23 b: according to the content of the descriptor of the tensordata to be synchronized, obtaining the tensor data to be synchronizedfrom the shared storage space.

For example, if the storage address of the tensor data to besynchronized is in the shared storage space, the second processor canaccess the data from the shared storage space. In this case, thesynchronization instruction may include the storage address of thetensor data to be synchronized. After receiving the synchronizationinstruction, the second processor may parse the instruction to obtainthe storage address of the tensor data to be synchronized; according tothe storage address of the tensor data, the second processor may createor modify the descriptor corresponding to the tensor data. According tothe content of the descriptor, the second processor may obtain thetensor data to be synchronized from the shared storage space, therebyachieving the entire synchronization process.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, unnecessary data transfer can be avoided, thetimes of accessing tensor data can be reduced, the processing efficiencyof synchronization can be improved, and the instruction compatibilityduring transfer and processing process can be realized.

FIG. 3b 2 shows a flowchart of a data synchronization method accordingto an embodiment of the present disclosure. The data synchronizationmethod can be applied to a second processor.

As shown in FIG. 3b 2, the data synchronization method includes:

a step S31 b: when there is tensor data to be synchronized, generating asynchronization request instruction, where the synchronization requestinstruction is used to instruct a first processor to determine adescriptor of the tensor data to be synchronized, and the descriptor isused to indicate a shape of the tensor data to be synchronized; and

a step S32 b: sending the synchronization request instruction to thefirst processor.

For example, when there is tensor data to be synchronized in the secondprocessor, the second processor may actively send the synchronizationrequest instruction to the first processor to obtain the tensor data tobe synchronized. The second processor may generate the synchronizationrequest instruction according to the information of the tensor data tobe synchronized, for example, the data characteristics of the tensordata to be synchronized. The present disclosure does not limit thespecific content of the synchronization request instruction. Accordingto the information in the synchronization request instruction, the firstprocessor may determine the descriptor of the tensor data to besynchronized, and then generate the synchronization instruction.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the synchronization request can be issuedactively when synchronization is needed, which may improve theefficiency of data synchronization.

In some embodiments, the synchronization request instruction includesthe data characteristics of the tensor data to be synchronized, so thatthe first processor can determine the tensor data to be synchronized,where the data characteristics of tensor data may include theidentifier, shape, source, and address of the tensor data. Those skilledin the art can set the data characteristics of the tensor data to besynchronized according to the actual situation, which is not limited inthe present disclosure.

In some embodiments, the data synchronization method further includes:

parsing the synchronization instruction from the first processor toobtain the synchronization information of the tensor data to besynchronized;

determining the descriptor of the tensor data to be synchronizedaccording to the synchronization information of the tensor data to besynchronized; and

obtaining the tensor data to be synchronized according to the descriptorof the tensor data to be synchronized.

For example, when receiving the synchronization instruction from thefirst processor, the second processor may parse the synchronizationinstruction to obtain the synchronization information (such as anidentifier, shape, and storage address of the tensor data) of the tensordata to be synchronized.

In some embodiments, if the synchronization instruction includes onlythe identifier of the descriptor, the second processor may internallysearch for the tensor data corresponding to the identifier of the tensordata and/or the descriptor corresponding to the tensor data, and thenobtain the tensor data to be synchronized according to the content ofthe descriptor, thereby achieving synchronization of the tensor data.

In some embodiments, if the synchronization instruction includes theshape and storage address of the tensor data, the second processor mayregister a descriptor indicating the tensor data to be synchronizedaccording to the shape and storage address of the tensor data, andobtain the tensor data to be synchronized according to the content ofthe descriptor, so as to realize the synchronization of the tensor data.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the complexity of data synchronization can bereduced, and the efficiency of data synchronization can be improved.

In some embodiments, the synchronization information includes thestorage address of the tensor data to be synchronized.

The step of determining the descriptor of the tensor data to besynchronized according to the synchronization information of the tensordata to be synchronized may include: determining the identifier of thedescriptor of the tensor data to be synchronized and/or the content ofthe descriptor according to the storage address of the tensor data to besynchronized. The step of obtaining the tensor data to be synchronizedaccording to the descriptor of the tensor data to be synchronized mayinclude: according to the content of the descriptor of the tensor datato be synchronized, obtaining the tensor data to be synchronized fromthe shared storage space.

For example, if the storage address of the tensor data to besynchronized is in the shared storage space, the second processor canaccess the data from the shared storage space. In this case, thesynchronization instruction may include the storage address of thetensor data to be synchronized. After receiving the synchronizationinstruction, the second processor may parse the instruction to obtainthe storage address of the tensor data to be synchronized; according tothe storage address of the tensor data, the second processor may createor modify the descriptor corresponding to the tensor data. According tothe content of the descriptor, the second processor may obtain thetensor data to be synchronized from the shared storage space, therebyachieving the entire synchronization process.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, unnecessary data transfer can be avoided, thetimes of accessing tensor data can be reduced, the processing efficiencyof synchronization can be improved, and the instruction compatibilityduring transfer and processing process can be realized.

In some embodiments, the identifier and content of the descriptor can bestored in the descriptor storage space, where the descriptor storagespace can be a storage space in an internal memory (such as a register,an on-chip SRAM, or other medium cache, etc.) of the processor. The datastorage space of the tensor data indicated by the descriptor may be astorage space in the internal memory (such as an on-chip cache) of theprocessor or a storage space in an external memory (an off-chip memory)connected to the processor. The data address in the data storage spacemay be an actual physical address or a virtual address. The presentdisclosure does not limit a position of the descriptor storage space anda position of the data storage space, and the type of the data address.

In some embodiments, the identifier and content of the descriptor, andtensor data indicated by the descriptor can be stored in a same area.For example, a continuous area of an on-chip cache with addressesADDR0-ADDR1023 can be used to store the above information. Within thisarea, addresses ADDR0-ADDR31 can be used to store the identifier of thedescriptor, addresses ADDR32-ADDR63 can be used to store the content ofthe descriptor, and addresses ADDR64-ADDR1023 can be used to store thetensor data indicated by the descriptor. The address ADDR is not limitedto 1 bit or 1 byte, and is an address unit used to represent an address.Those skilled in the art can determine the storage area and the addressthereof according to the specific applications, which is not limited inthe present disclosure.

In some embodiments, the identifier and content of the descriptor, andthe tensor data indicated by the descriptor can be respectively storedin different areas of an internal memory. For example, a register can beused as a descriptor storage space to store the identifier and contentof the descriptor, and an on-chip cache can be used as a data storagespace to store the tensor data indicated by the descriptor.

In some embodiments, a special register (SR) may be provided for thedescriptor, where data in the descriptor may be an immediate number orbe obtained from the special register. When the register is used tostore the identifier and content of the descriptor, a serial number ofthe register can be used to indicate the identifier of the descriptor.For example, if the serial number of the register is 0, the identifierof a descriptor stored in the register is 0. When the descriptor in theregister is valid, an area can be allocated in a caching space (such ascreating a tensor caching unit for each piece of tensor data in thecache) according to a size of tensor data indicated by the descriptorfor storing the tensor data. It should be understood that a presetcaching space may also be used to store the tensor data, which is notlimited in the present disclosure.

In some embodiments, the identifier and content of the descriptor can bestored in an internal memory, and the tensor data indicated by thedescriptor can be stored in an external memory. For example, theidentifier and content of the descriptor may be stored on-chip and thetensor data indicated by the descriptor may be stored off-chip.

In some embodiments, the data address of the data storage spacecorresponding to the descriptor may be a fixed address. For example, aseparate data storage space may be allocated for the tensor data, and astart address of each piece of tensor data in the data storage spacecorresponds to an identifier of the descriptor. In this case, theprocessor can determine the data address of the tensor data according tothe content of the descriptor.

In some embodiments, when the data address of the data storage spacecorresponding to the identifier of the descriptor is a variable address,the descriptor may be also used to indicate the address of N-dimensionaltensor data, where the content of the descriptor may further include atleast one address parameter representing the address of the tensor data.For example, if the tensor data is 3-dimensional data, when thedescriptor points to the address of the tensor data, the content of thedescriptor may include an address parameter indicating the address ofthe tensor data, such as a start address of the tensor data; or thecontent of the descriptor may include a plurality of address parametersof the address of the tensor data, such as a start address+addressoffset of the tensor data, or address parameters of the tensor data ineach dimension. Those skilled in the art can set the address parametersaccording to actual needs, which is not limited in the presentdisclosure.

In some embodiments, the address parameter of the tensor data includes abase address of the datum point of the descriptor in the data storagespace of the tensor data, where the base address may vary from differentdatum points. The present disclosure does not limit the selection of thedatum point.

In some embodiments, the base address may include a start address of thedata storage space. When the datum point of the descriptor is a firstdata block of the data storage space, the base address of the descriptoris the start address of the data storage space. When the datum point ofthe descriptor is other data than the first data block in the datastorage space, the base address of the descriptor is the physicaladdress of the data block in the data storage space.

In some embodiments, the shape parameter of the tensor data includes atleast one of the followings: a size of the data storage space of thetensor data in at least one of N dimensions, a size of the storage areain at least one of the N dimensions, an offset of the storage area in atleast one of the N dimensions, a position of at least two vertices atdiagonal positions in the N dimensions relative to the datum point, anda mapping relationship between a data description position of the tensordata indicated by the descriptor and the data address of the tensor dataindicated by the descriptor. The data description position is a mappingposition of a point or an area in the tensor data indicated by thedescriptor, for example, if the tensor data is 3-dimensional data, thedescriptor can use a coordinate (x, y, z) to represent the shape of thetensor data, and the data description position of the tensor data can berepresented by the coordinate (x, y, z), and the data descriptionposition of the tensor data may be a position of a point or an areawhere the tensor data is mapped in a 3-dimensional space.

It should be understood that those skilled in the art may select a shapeparameter representing tensor data according to actual conditions, whichis not limited in the present disclosure.

It should be noted that, for the sake of simple description, the abovemethod embodiments are all described as a series of action combinations.However, those skilled in the art should be aware that the presentdisclosure is not limited by the described action order, becauseaccording to the present disclosure, certain steps may be executed inanother order or executed simultaneously. Those skilled in the artshould also be aware that the embodiments described in the specificationare alternative embodiments and that the actions and modules involvedare not necessary in the present disclosure.

It should be further noted that although the steps in the flow chartsare shown in sequence as indicated by the arrows, these steps are notnecessarily executed in the order indicated by the arrows. Unlessspecifically stated in the present disclosure, the execution of thesesteps is not strictly limited in order, and these steps may be executedin other orders. In addition, at least part of the steps in in the flowcharts may include a plurality of sub-steps or stages. These sub-stepsor stages are not necessarily executed at the same time, but may beexecuted at different times. The execution of these sub-steps or stagesis not necessarily performed sequentially, but may be performedalternately with other steps or at least a part of the sub-steps orstages of other steps.

FIG. 3b 3 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure. The datasynchronization apparatus is applied to the first processor. As shown inFIG. 3b 3, the data synchronization apparatus includes:

a first information determining module 51 b configured to determinesynchronization information of tensor data according to a descriptor ofthe tensor data to be synchronized, where the descriptor is used toindicate a shape of the tensor data to be synchronized;

a first instruction generating module 52 b configured to generate asynchronization instruction according to the synchronization informationof the tensor data; and

a first instruction sending module 53 b configured to send thesynchronization instruction to a second processor, where thesynchronization instruction is used to instruct the second processor toobtain the tensor data to be synchronized according to thesynchronization instruction.

In some embodiments, the synchronization information includes a storageaddress of the tensor data to be synchronized. The first instructiongenerating module includes: a first generating sub-module configured to,when the storage address of the tensor data to be synchronized is in ashared storage space, generate the synchronization instruction accordingto the storage address of the tensor data to be synchronized, so as toinstruct the second processor to obtain the tensor data to besynchronized from the shared storage space.

In some embodiments, the synchronization information includes thestorage address of the tensor data to be synchronized. The firstinstruction generating module includes: a transferring sub-moduleconfigured to, when the storage address of the tensor data to besynchronized is in a non-shared storage space, store the tensor data tobe synchronized in the shared storage space; and a second generatingsub-module configured to, according to the address of the tensor data tobe synchronized in the shared storage space, generate thesynchronization instruction, so as to instruct the second processor toobtain the tensor data to be synchronized from the shared storage space.

In some embodiments, the data synchronization apparatus furtherincludes: a first descriptor determining module configured to, accordingto the synchronization request instruction from the second processor,determine the descriptor of the tensor data to be synchronized.

In some embodiments, the synchronization request instruction includesdata characteristics of the tensor data to be synchronized. The firstdescriptor determining module includes: an instruction parsingsub-module configured to parse the synchronization request instructionto obtain the data characteristics of the tensor data to besynchronized; and a first descriptor determining sub-module configuredto determine the descriptor of the tensor data to be synchronizedaccording to the data characteristics of the tensor data to besynchronized.

FIG. 3b 4 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure. The datasynchronization apparatus is applied to a second processor. As shown inFIG. 3b 4, the data synchronization apparatus includes:

a second information determining module 61 b configured to parse asynchronization instruction from a first processor to obtainsynchronization information of tensor data to be synchronized:

a second descriptor determining module 62 b configured to determine adescriptor of the tensor data to be synchronized according to thesynchronization information of the tensor data to be synchronized, wherethe descriptor is used to indicate a shape of the tensor data to besynchronized; and

a first data obtaining module 63 b configured to obtain the tensor datato be synchronized according to the descriptor of the tensor data to besynchronized.

In some embodiments, the synchronization information includes a storageaddress of the tensor data to be synchronized. The second descriptordetermining module includes: a first determining sub-module configuredto determine the identifier of the descriptor of the tensor data to besynchronized and/or the content of the descriptor according to thestorage address of the tensor data to be synchronized. The first dataobtaining module includes: a first data obtaining sub-module configuredto, according to the content of the descriptor of the tensor data to besynchronized, obtain the tensor data to be synchronized from the sharedstorage space.

FIG. 3b 5 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure. The datasynchronization apparatus is applied to a second processor. As shown inFIG. 3b 5, the data synchronization apparatus includes:

a second instruction generating module 71 b configured to, when there istensor data to be synchronized, generate a synchronization requestinstruction, where the synchronization request instruction is used toinstruct a first processor to determine a descriptor of the tensor datato be synchronized, and the descriptor is used to indicate a shape ofthe tensor data to be synchronized; and

a second instruction sending module 72 b configured to send thesynchronization request instruction to the first processor.

In some embodiments, the synchronization request instruction includesdata characteristics of the tensor data to be synchronized.

In some embodiments, the data synchronization apparatus furtherincludes: a third information determining module configured to parse thesynchronization instruction from the first processor to obtain thesynchronization information of the tensor data to be synchronized; athird descriptor determining module configured to determine thedescriptor of the tensor data to be synchronized according to thesynchronization information of the tensor data to be synchronized; and asecond data obtaining module configured to obtain the tensor data to besynchronized according to the descriptor of the tensor data to besynchronized.

In some embodiments, the synchronization information includes a storageaddress of the tensor data to be synchronized. The third descriptordetermining module includes: a second determining sub-module configuredto determine the identifier of the descriptor of the tensor data to besynchronized and/or the content of the descriptor according to thestorage address of the tensor data to be synchronized. The second dataobtaining module includes a second data obtaining sub-module configuredto, according to the content of the descriptor of the tensor data to besynchronized, obtain the tensor data to be synchronized from the sharedstorage space.

It should be understood that the apparatus embodiment described above isonly schematic, and the apparatus provided in the present disclosure maybe implemented in other manners. For example, division of theunits/modules is only logical function division and another divisionmanner may be adopted during practical implementation. For example, aplurality of units or components may be combined or integrated intoanother system or some characteristics may be neglected or notperformed.

In addition, unless otherwise specified, each functional unit/module inthe embodiments of the disclosure may be integrated into a unit/module,each unit/module may also physically exist independently, and two ormore units/modules may also be integrated into one unit/module. Theintegrated unit/module may be implemented in the form of hardware or asoftware functional unit/module.

If the integrated unit/module is implemented in the form of hardware,the hardware may be a digital circuit, an analogue circuit, and thelike. The physical implementation of hardware may include, but is notlimited to, a transistor, a memristor, and the like. Unless otherwisespecified, the artificial intelligence processor may be any appropriatehardware processor, such as CPU, GPU, FPGA, DSP, ASIC, and the like.Unless otherwise specified, the storage unit may be any proper magneticstorage medium or magneto-optic storage medium, for example, an RRAM(Resistive Random Access Memory), a DRAM (Dynamic Random Access Memory),an SRAM (Static Random-Access Memory), an EDRAM (Enhanced Dynamic RandomAccess Memory), an HBM (High-Bandwidth Memory), an HMC (Hybrid MemoryCube), and the like.

If being implemented in the form of a software program module and soldor used as an independent product, the integrated unit/module may bestored in a computer-readable memory. Based on such an understanding,all or part of the technical solutions may be embodied in form ofsoftware product. The computer software product is stored in a memory,including a plurality of instructions configured to enable a computerdevice (which may be a PC, a server, a network device, or the like) toperform all or part of the operations of the method in each embodimentof the application. The memory may include various media capable ofstoring program codes such as a U disk, a mobile hard disk, a read-onlymemory (ROM), a random access memory (RAM), a magnetic disk, or anoptical disk.

In some embodiments, the present disclosure provides an artificialintelligence chip including the above-mentioned data synchronizationapparatus.

In some embodiments, the present disclosure provides a board cardincluding a storage device, an interface apparatus, a control device,and the above-mentioned artificial intelligence chip. The artificialintelligence chip is connected to the storage device, the controldevice, and the interface apparatus, respectively; the storage device isconfigured to store data; the interface apparatus is configured toimplement data transfer between the artificial intelligence chip and anexternal equipment; and the control device is configured to monitor astate of the artificial intelligence chip.

In the above-mentioned embodiments, the description of each embodimenthas its own focus. For parts that are not described in detail in anembodiment, please refer to related descriptions of other embodiments.The technical features of the above-mentioned embodiments may becombined arbitrarily. In order to make the description concise, not allpossible combinations of the various technical features in theabove-mentioned embodiments are described. However, as long as there isno contradiction in the combinations of these technical features, theyshould be regarded as the scope of this specification.

The foregoing may be better understood according to the followingarticles:

A1. A data synchronization method applied to a first processor,comprising:

determining synchronization information of tensor data according to adescriptor of the tensor data to be synchronized, wherein the descriptoris used to indicate a shape of the tensor data to be synchronized;

generating a synchronization instruction according to thesynchronization information of the tensor data; and

sending the synchronization instruction to a second processor, whereinthe synchronization instruction is used to instruct the second processorto obtain the tensor data to be synchronized according to thesynchronization instruction.

A2. The data synchronization method of A1, wherein the synchronizationinformation includes a storage address of the tensor data to besynchronized, wherein the generating a synchronization instructionaccording to the synchronization information of the tensor dataincludes:

when the storage address of the tensor data to be synchronized is in ashared storage space, generating the synchronization instructionaccording to the storage address of the tensor data to be synchronized,so as to instruct the second processor to obtain the tensor data to besynchronized from the shared storage space.

A3. The data synchronization method of A1 or A2, wherein thesynchronization information includes the storage address of the tensordata to be synchronized, wherein the generating a synchronizationinstruction according to the synchronization information of the tensordata includes:

when the storage address of the tensor data to be synchronized is in anon-shared storage space, storing the tensor data to be synchronized inthe shared storage space; and

according to the address of the tensor data to be synchronized in theshared storage space, generating the synchronization instruction toinstruct the second processor to obtain the tensor data to besynchronized from the shared storage space.

A4. The data synchronization method of any one of A1-A3, furthercomprising:

determining the descriptor of the tensor data to be synchronizedaccording to a synchronization request instruction from the secondprocessor.

A5. The data synchronization method of A4, wherein the synchronizationrequest instruction includes data characteristics of the tensor data tobe synchronized, wherein the determining the descriptor of the tensordata to be synchronized according to the synchronization requestinstruction from the second processor includes:

parsing the synchronization request instruction to obtain the datacharacteristics of the tensor data to be synchronized; and

determining the descriptor of the tensor data to be synchronizedaccording to the data characteristics of the tensor data to besynchronized.

A6. A data synchronization method applied to a second processor,comprising:

parsing a synchronization instruction from a first processor to obtainsynchronization information of tensor data to be synchronized;

determining a descriptor of the tensor data to be synchronized accordingto the synchronization information of the tensor data to besynchronized, wherein the descriptor is used to indicate a shape of thetensor data to be synchronized; and

obtaining the tensor data to be synchronized according to the descriptorof the tensor data to be synchronized.

A7. The data synchronization method of A6, wherein the synchronizationinformation includes a storage address of the tensor data to besynchronized, wherein

the determining a descriptor of the tensor data to be synchronizedaccording to the synchronization information of the tensor data to besynchronized includes: determining an identifier of the descriptor ofthe tensor data to be synchronized and/or content of the descriptoraccording to the storage address of the tensor data to be synchronized;and

the obtaining the tensor data to be synchronized according to thedescriptor of the tensor data to be synchronized includes: according tothe content of the descriptor of the tensor data to be synchronized,obtaining the tensor data to be synchronized from a shared storagespace.

A8. A data synchronization method applied to a second processor,comprising:

when there is tensor data to be synchronized, generating asynchronization request instruction, wherein the synchronization requestinstruction is used to instruct a first processor to determine adescriptor of the tensor data to be synchronized, and the descriptor isused to indicate a shape of the tensor data to be synchronized; and

sending the synchronization request instruction to the first processor.

A9. The data synchronization method of A8, wherein the synchronizationrequest instruction includes data characteristics of the tensor data tobe synchronized.

A10. The data synchronization method of A8 or A9, further comprising:

parsing a synchronization instruction from the first processor to obtainsynchronization information of the tensor data to be synchronized;

determining the descriptor of the tensor data to be synchronizedaccording to the synchronization information of the tensor data to besynchronized; and

obtaining the tensor data to be synchronized according to the descriptorof the tensor data to be synchronized.

A11. The data synchronization method of A10, wherein the synchronizationinformation includes a storage address of the tensor data to besynchronized, wherein

the determining the descriptor of the tensor data to be synchronizedaccording to the synchronization information of the tensor data to besynchronized includes: determining an identifier of the descriptor ofthe tensor data to be synchronized and/or content of the descriptoraccording to the storage address of the tensor data to be synchronized;and

the obtaining the tensor data to be synchronized according to thedescriptor of the tensor data to be synchronized includes: according tothe content of the descriptor of the tensor data to be synchronized,obtaining the tensor data to be synchronized from a shared storagespace.

A12. A data synchronization apparatus applied to a first processor,comprising:

a first information determining module configured to determinesynchronization information of tensor data according to a descriptor ofthe tensor data to be synchronized, wherein the descriptor is used toindicate a shape of the tensor data to be synchronized:

a first instruction generating module configured to generate asynchronization instruction according to the synchronization informationof the tensor data; and

a first instruction sending module configured to send thesynchronization instruction to a second processor, where thesynchronization instruction is used to instruct the second processor toobtain the tensor data to be synchronized according to thesynchronization instruction.

A13. The data synchronization apparatus of A12, wherein thesynchronization information includes a storage address of the tensordata to be synchronized, wherein the first instruction generating moduleincludes:

a first generating sub-module configured to, when the storage address ofthe tensor data to be synchronized is in a shared storage space,generate the synchronization instruction according to the storageaddress of the tensor data to be synchronized, so as to instruct thesecond processor to obtain the tensor data to be synchronized from theshared storage space.

A14. The data synchronization apparatus of A12 or A13, wherein thesynchronization information includes the storage address of the tensordata to be synchronized, wherein the first instruction generating moduleincludes:

a transferring sub-module configured to, when the storage address of thetensor data to be synchronized is in a non-shared storage space, storethe tensor data to be synchronized in the shared storage space; and

a second generating sub-module configured to, according to the addressof the tensor data to be synchronized in the shared storage space,generate the synchronization instruction to instruct the secondprocessor to obtain the tensor data to be synchronized from the sharedstorage space.

A15. The data synchronization apparatus of any one of A12-A14, furthercomprising:

a first descriptor determining module configured to, according tosynchronization request instruction from the second processor, determinethe descriptor of the tensor data to be synchronized.

A16. The data synchronization apparatus of A15, wherein thesynchronization request instruction includes data characteristics of thetensor data to be synchronized, wherein the first descriptor determiningmodule includes:

an instruction parsing sub-module configured to parse thesynchronization request instruction to obtain the data characteristicsof the tensor data to be synchronized; and

a first descriptor determining sub-module configured to determine thedescriptor of the tensor data to be synchronized according to the datacharacteristics of the tensor data to be synchronized.

A17. A data synchronization apparatus applied to a second processor,comprising:

a second information determining module configured to parse asynchronization instruction from a first processor to obtainsynchronization information of tensor data to be synchronized;

a second descriptor determining module configured to determine adescriptor of the tensor data to be synchronized according to thesynchronization information of the tensor data to be synchronized, wherethe descriptor is used to indicate a shape of the tensor data to besynchronized; and

a first data obtaining module configured to obtain the tensor data to besynchronized according to the descriptor of the tensor data to besynchronized.

A18. The data synchronization apparatus of A17, wherein thesynchronization information includes a storage address of the tensordata to be synchronized, wherein

the second descriptor determining module includes: a first determiningsub-module configured to determine an identifier of the descriptor ofthe tensor data to be synchronized and/or content of the descriptoraccording to the storage address of the tensor data to be synchronized;and

the first data obtaining module includes: a first data obtainingsub-module configured to, according to the content of the descriptor ofthe tensor data to be synchronized, obtain the tensor data to besynchronized from a shared storage space.

A19. A data synchronization apparatus applied to a second processor,comprising:

a second instruction generating module configured to, when there istensor data to be synchronized, generate a synchronization requestinstruction, where the synchronization request instruction is used toinstruct a first processor to determine a descriptor of the tensor datato be synchronized, and the descriptor is used to indicate a shape ofthe tensor data to be synchronized; and

a second instruction sending module configured to send thesynchronization request instruction to the first processor.

A20. The data synchronization apparatus of A19, wherein thesynchronization request instruction includes data characteristics of thetensor data to be synchronized.

A21. The data synchronization apparatus of A19 or A20, furthercomprising:

a third information determining module configured to parse thesynchronization instruction from the first processor to obtainsynchronization information of the tensor data to be synchronized;

a third descriptor determining module configured to determine thedescriptor of the tensor data to be synchronized according to thesynchronization information of the tensor data to be synchronized; and

a second data obtaining module configured to obtain the tensor data tobe synchronized according to the descriptor of the tensor data to besynchronized.

A22. The data synchronization apparatus of A21, wherein thesynchronization information includes a storage address of the tensordata to be synchronized, wherein

the third descriptor determining module includes: a second determiningsub-module configured to determine an identifier of the descriptor ofthe tensor data to be synchronized and/or content of the descriptoraccording to the storage address of the tensor data to be synchronized;and

the second data obtaining module includes: a second data obtainingsub-module configured to, according to the content of the descriptor ofthe tensor data to be synchronized, obtain the tensor data to besynchronized from a shared storage space.

A23. An artificial intelligence chip, comprising the datasynchronization apparatus of any one of A12-A22.

A24. An electronic device, comprising the artificial intelligence chipof A23.

A25. A board card, comprising a storage device, an interface apparatus,a control device, and the artificial intelligence chip of A23, wherein

the artificial intelligence chip is connected to the storage device, thecontrol device, and the interface apparatus, respectively;

the storage device is configured to store data;

the interface apparatus is configured to implement data transfer betweenthe artificial intelligence chip and an external equipment; and

the control device is configured to monitor a state of the artificialintelligence chip.

A26. The board card of A25, wherein

the storage device includes a plurality of groups of storage units,wherein each group of the storage units is connected with the artificialintelligence chip by a bus, and the storage units are DDR SDRAMs:

the chip includes a DDR controller configured to control data transferand data storage of each storage unit; and

the interface apparatus is a standard PCIE interface.

With the continuous development of the A1 (Artificial Intelligence)technology, it has gradually obtained wide application and worked wellin the fields of image recognition, speech recognition, and naturallanguage processing, and the like. However, as the complexity of A1algorithms is growing, the amount of data and data dimensions that needto be processed are increasing, therefore, multi-core and/or multi-chipdata are usually required for data processing. When data is synchronizedbetween cores or chips, a synchronization method adopting therelated-art may result in large synchronization overhead and lowprocessing efficiency.

In some embodiments, the present disclosure provides a datasynchronization method.

FIG. 1e shows a flowchart of a data synchronization method according toan embodiment of the present disclosure. As shown in FIG. 1e , the datasynchronization method is applied to a first processor (any processor ina processing system), and the method includes:

a step S11 e: according to a descriptor of tensor data to besynchronized, generating a state query instruction, where the descriptoris used to indicate a shape of the tensor data to be synchronized, andthe state query instruction is used to instruct a second processor todetermine the amount of tensor data to be synchronized and generate asynchronization state instruction, where the state query instructionincludes an identifier of the descriptor and/or content of thedescriptor;

a step S12 e: sending the state query instruction to the secondprocessor.

For example, the data to be synchronized may include N-dimensionaltensor data (N is an integer greater than or equal to 0, for example,N=1, 2, or 3).

In some embodiments, during data processing, data synchronizationbetween a plurality of processors (such as a plurality of cores of anartificial intelligence chip) may be executed, for example, an operationresult of a processor A1 may be synchronized to a processor A2 as inputdata of another operation. In this case, a data synchronizationmechanism based on the descriptor can be used to achieve datasynchronization.

In some embodiments, since a non-shared storage space of each processorallocated to the tensor data to be synchronized may be limited, thetensor data cannot be synchronized at the same time. In this case, partof tensor data can be synchronized firstly, and repeated many timesuntil all of the tensor data are synchronized.

In some embodiments, the first processor among a plurality of processorsmay be set as the sender of data to be synchronized, and the secondprocessor may be set as the receiver of data synchronization. Both thefirst processor and the second processor are any of the plurality ofprocessors, and the second processor may be of the same type ordifferent from the first processor. The present disclosure does notlimit the type of the first processor and the type of the secondprocessor.

In some embodiments, when there is tensor data to be synchronized in thesender of data to be synchronized, for example, when the first processorcompletes an operation to obtain the result (tensor data) of theoperation, the sender can query the state of the receiver to determinethe amount of data that can be contained in the non-shared storage spaceof the receiver allocated to the tensor data, so that part of tensordata can be synchronized. The first processor among a plurality ofprocessors may be set as the sender of data to be synchronized, and thesecond processor may be set as the receiver of data synchronization.Both the first processor and the second processor are any of theplurality of processors, and the second processor may be of the sametype or different from the first processor. The present disclosure doesnot limit the type of the first processor and the type of the secondprocessor.

In some embodiments, in the step S11, the first processor may generatethe state query instruction according to the descriptor of the tensordata to be synchronized. The state query instruction may include anidentifier of the descriptor of the tensor data to be synchronizedand/or content of the descriptor, and the state query instruction isused to instruct the second processor to determine and reply its ownstate (the amount of data that can be synchronized among the tensordata).

In some embodiments, in the step S12 e, the first processor may send thestate query instruction to the second processor. After receiving thestate query instruction, the second processor may parse the instructionto determine the identifier of the descriptor and/or the content of thedescriptor. According to the identifier of the descriptor and/or thecontent of the descriptor, the second processor may determine the tensordata to be synchronized, and then determine the space that can beallocated to the tensor data, and determine the amount of data that canbe synchronized among the tensor data. According to the amount of datathat can be synchronized among the tensor data and descriptor, thesecond processor can generate and send a synchronization stateinstruction, so that the first processor can determine the descriptor ofthe tensor data to be synchronized and the amount of data that can besynchronized of this time.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the sender of data to be synchronized mayactively query the state of the receiver, so that part of data can besynchronized between the sender and the receiver, thereby improving theefficiency of data synchronization.

In some embodiments, the data synchronization method further includes:

when the synchronization state instruction is received from the secondprocessor, determining first sub-data of the tensor data according tothe descriptor of the tensor data in the synchronization stateinstruction and the amount of data that can be synchronized, where theamount of the first sub-data corresponds to the amount of data that canbe synchronized; and

according to the first sub-data, generating a descriptor synchronizationinstruction and sending the descriptor synchronization instruction tothe second processor to instruct the second processor to obtain thefirst sub-data.

For example, when receiving the synchronization state instruction fromthe second processor, the first processor may parse the instruction toobtain the content of the instruction (for example, the identifier ofthe descriptor, the amount of data that can be synchronized, etc.).According to the identifier of the descriptor, the descriptor of thetensor data to be synchronized can be determined, so as to determine thetensor data to be synchronized; and the part of data that can besynchronized this time (the first sub-data) is determined from thetensor data according to the amount of data that can be synchronized.The amount of the first sub-data may correspond to the amount of datathat can be synchronized, for example, the amount of the first sub-datamay be less than or equal to the amount of data that can besynchronized.

In some embodiments, if all the data of the tensor data has not beensynchronized, data that can be synchronized may be selected from thetensor data as the first sub-data. If part of the tensor data has notbeen synchronized, and the amount of data that has not been synchronizedis greater than the amount of data that can be synchronized, data thatcan be synchronized may be selected from the data that has not beensynchronized (second sub-data of the tensor data) as the first sub-data;if the amount of data that has not been synchronized is less than orequal to the amount of data that can be synchronized, the data that hasnot been synchronized can be directly taken as the first sub-data. Itshould be understood that those skilled in the art can determine thefirst sub-data according to the actual situation, which is not limitedin the present disclosure.

In some embodiments, the synchronization state instruction may alsoinclude a range of part of tensor data to be synchronized, such as thecontent of the descriptor of the part of sub-data or a range of astorage address of the part of sub-data, so as to determine the part ofdata to be synchronized. The first processor may directly determine thefirst sub-data to be synchronized according to the range of the part ofdata.

In some embodiments, the first processor may generate a descriptorsynchronization instruction according to the first sub-data and send thedescriptor synchronization instruction to the second processor. Thedescriptor synchronization instruction may include the identifier of thedescriptor of the tensor data to be synchronized and the first sub-data.After receiving the descriptor synchronization instruction, the secondprocessor may parse the descriptor synchronization instruction todetermine the descriptor of the tensor data to be synchronized and thefirst sub-data of the tensor data, determine the tensor data to besynchronized according to the descriptor, and store the first sub-dataof the tensor data in its own non-shared storage space.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the tensor data can be determined according tothe descriptor in the synchronization state instruction, the sub-datasynchronized this time can be determined according to the amount of datathat can be synchronized of the receiver, and the descriptorsynchronization instruction can be generated and sent according to thesub-data, so that the receiver can obtain the sub-data synchronized thistime, thereby reducing the synchronization overhead and improving theefficiency of data synchronization.

In some embodiments, the synchronization state instruction includes theidentifier of the descriptor. The step of determining first sub-data ofthe tensor data according to the descriptor of the tensor data in thesynchronization state instruction and the amount of data that can besynchronized when the synchronization state instruction is received fromthe second processor includes:

parsing the synchronization state instruction to obtain the identifierof the descriptor and the amount of data that can be synchronized; and

according to the identifier of the descriptor, determining thedescriptor of the tensor data to be synchronized.

For example, the synchronization state instruction may include theidentifier of the descriptor (for example, the identifier is TR1) andthe amount of data that can be synchronized. The first processor mayparse the synchronization state instruction to obtain the identifier ofthe descriptor and the amount of data that can be synchronized, and thendetermine the descriptor of the tensor data to be synchronized accordingto the identifier of the descriptor.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the amount of data transmitted duringsynchronization can be reduced, and the processing efficiency can beimproved.

In some embodiments, the step of determining first sub-data of thetensor data according to the descriptor of the tensor data in thesynchronization state instruction and the amount of data that can besynchronized when the synchronization state instruction is received fromthe second processor includes:

determining the tensor data and second sub-data to be synchronized amongthe tensor data according to the descriptor of the tensor data; and

determining the first sub-data according to the second sub-data and theamount of data that can be synchronized in the synchronization stateinstruction.

For example, the state of data among the tensor data may be set, wherethe data that has been synchronized may be set to a synchronized state,and the data that has not been synchronized may be set to ato-be-synchronized state. In this case, when receiving thesynchronization state instruction from the second processor, the firstprocessor may determine the tensor data to be synchronized according tothe descriptor; according to the state of the data among the tensordata, the first processor may determine the second sub-data in theto-be-synchronized state; and according to the second sub-data and theamount of data that can be synchronized indicated by the synchronizationstate instruction, the first processor may determine the first sub-datato be synchronized this time.

In some embodiments, if the amount of the second sub-data is greaterthan the amount of data that can be synchronized, the first sub-datasynchronized this time can be selected from the second sub-data; if theamount of the second sub-data is less than or equal to the amount ofdata that can be synchronized, the second sub-data can be directly takenas the first sub-data.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, part of tensor data synchronized this time canbe determined, and then the part of tensor data can be synchronized,thereby improving the efficiency of data synchronization.

In some embodiments, the data synchronization method further includes:changing a state of the first sub-data of the tensor data from a to-besynchronized state to a synchronized state.

For example, after the first processor generates the descriptorsynchronization instruction according to the first sub-data of thetensor data and sends the descriptor synchronization instruction to thesecond processor to make the second processor synchronize the firstsub-data of the tensor data, the first processor can change the state ofdata among the tensor data, in other words, the first processor canchange the state of the first sub-data from the to-be synchronized stateto the synchronized state. In this way, when the state of the secondprocessor is queried next time and the synchronization state instructionof the second processor is received, data to be synchronized next timecan be determined from part of data in the to-be-synchronized state,thereby avoiding repeated data synchronization and improving datasynchronization efficiency.

FIG. 3c shows a flowchart of a data synchronization method according toan embodiment of the present disclosure. As shown in FIG. 3 cd, the datasynchronization method is applied to a second processor, and the methodincludes:

a step S31 c: when a state query instruction is received from a firstprocessor, determining a descriptor of tensor data to be synchronized,where the descriptor is used to indicate a shape of the tensor data tobe synchronized;

a step S32 c: determining the amount of data that can be synchronizedamong the tensor data according to the descriptor of the tensor data;

a step S33 c: according to the descriptor of the tensor data and theamount of data that can be synchronized, generating a synchronizationstate instruction, where the synchronization state instruction is usedto instruct the first processor to determine first sub-data of thetensor data, and the amount of the first sub-data corresponds to theamount of data that can be synchronized; and

a step S34 c: sending the synchronization state instruction to the firstprocessor.

For example, when there is tensor data to be synchronized in a sender ofdata to be synchronized, the sender may query the state of the receiver.The first processor (sender) may generate and send the state queryinstruction, and when the second processor receives the state queryinstruction in step S31 c, the second processor may parse the statequery instruction to determine the descriptor of the tensor data to besynchronized.

In some embodiments, in the step S32 c, the second processor maydetermine the tensor data to be synchronized according to thedescriptor, and determine the amount of data that can be contained inthe non-shared storage space of the second processor allocated to thetensor data, i.e., the amount of data can be synchronized, so that partof tensor data can be synchronized.

In some embodiments, in the step S33 c, the second processor maygenerate and send a synchronization state instruction to the firstprocessor according to the determined amount of data that can besynchronized and the descriptor of the tensor data to instruct the firstprocessor to determine the descriptor of the tensor data to besynchronized and the amount of data that can be synchronized this time.After determining the part of the data that can be synchronized thistime (i.e., the first sub-data), the first processor may generate thedescriptor synchronization instruction and send the descriptorsynchronization instruction to the second processor in step S34 c, wherethe descriptor synchronization instruction may include the identifier ofthe descriptor of the tensor data to be synchronized and the firstsub-data.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the sender may query the state of the receiver;after receiving the state query instruction, the receiver determines andresponds to its own state (i.e., the amount of data that can besynchronized). In this way, part of tensor data can be synchronizedthrough interaction, which may improve the efficiency of datasynchronization.

In some embodiments, the data synchronization method further includes:

when the descriptor synchronization instruction is received from thefirst processor, determining the descriptor of the tensor data to besynchronized and first sub-data of the tensor data; and

according to the descriptor of the tensor data, storing the firstsub-data of the tensor data.

For example, when receiving the descriptor synchronization instruction,the second processor may parse the instruction to determine thedescriptor of the tensor data to be synchronized and the first sub-dataof the tensor data to be synchronized this time; and then the secondprocessor may determine the tensor data to be synchronized according tothe descriptor, and store the first sub-data of the tensor data in itsown non-shared storage space.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the receiver can determine the descriptoraccording to the descriptor synchronization instruction and obtainsub-data synchronized this time, thereby reducing synchronizationoverhead and improving the efficiency of data synchronization.

In some embodiments, the receiver of data synchronization can issue asynchronization request for part of tensor data, in other words, thereceiver sends a descriptor synchronization request instruction, wherethe descriptor synchronization request instruction may determine thedescriptor of the tensor data to be synchronized and the amount of datathat can be synchronized among the tensor data, i.e., the amount of datathat can be contained in the non-shared storage space of the receiverallocated to the tensor data.

In some embodiments, the present disclosure provides a datasynchronization method applied to a first processor, including:

when a descriptor synchronization request instruction is received from asecond processor, determining a descriptor of tensor data to besynchronized and the amount of data that can be synchronized among thetensor data, where the descriptor is used to indicate a shape of thetensor data to be synchronized;

determining first sub-data of the tensor data according to thedescriptor of the tensor data and the amount of data that can besynchronized, where the amount of the first sub-data corresponds to theamount of data that can be synchronized; and

according to the first sub-data, generating a descriptor synchronizationinstruction and sending the descriptor synchronization instruction tothe second processor to instruct the second processor to obtain thefirst sub-data.

In some embodiments, when receiving the descriptor synchronizationrequest instruction from the second processor, the first processor mayparse the instruction to obtain content of the instruction (for example,an identifier of the descriptor of the tensor data to be synchronized,data characteristics of the tensor data to be synchronized, the amountof data that can be synchronized, and the like), thereby determining thedescriptor of the tensor data to be synchronized and the amount of datathat can be synchronized.

In some embodiments, the first processor may determine the tensor datato be synchronized according to the descriptor, and determine the partof data that can be synchronized this time from the tensor dataaccording to the amount of data that can be synchronized, i.e., thefirst sub-data. The amount of the first sub-data may correspond to theamount of data that can be synchronized, for example, the amount of thefirst sub-data may less than or equal to the amount of data that can besynchronized.

In some embodiments, if all the data of the tensor data has not beensynchronized, data that can be synchronized may be selected from thetensor data as the first sub-data. If part of the tensor data has notbeen synchronized, and the amount of data that has not been synchronizedis greater than the amount of data that can be synchronized, data thatcan be synchronized may be selected from the data that has not beensynchronized (second sub-data of the tensor data) as the first sub-data;if the amount of data that has not been synchronized is less than orequal to the amount of data that can be synchronized, the data that hasnot been synchronized can be directly taken as the first sub-data. Itshould be understood that those skilled in the art can determine thefirst sub-data according to the actual situation, which is not limitedin the present disclosure.

In some embodiments, the first processor may generate a descriptorsynchronization instruction according to the first sub-data and send thedescriptor synchronization instruction to the second processor, wherethe descriptor synchronization instruction may include the identifier ofthe descriptor of the tensor data to be synchronized and the firstsub-data of the tensor data. After receiving the descriptorsynchronization instruction, the second processor may parse theinstruction to determine the descriptor of the tensor data to besynchronized and the first sub-data of the tensor data, determine thetensor data to be synchronized according to the descriptor, and storethe first sub-data of the tensor data in its own non-shared storagespace.

According to the above-mentioned data synchronization method provided bythe present disclosure, by setting the descriptor indicating the shapeof the tensor data, the tensor data can be determined according to thedescriptor in the descriptor synchronization request instruction. Thesub-data synchronized this time can be determined according to theamount of data that can be synchronized of the receiver, and thedescriptor synchronization instruction can be generated and sentaccording to the sub-data, so that the receiver can obtain the sub-datasynchronized this time. In this way, the synchronization overhead can bereduced and the efficiency of data synchronization can be improved.

In some embodiments, the descriptor synchronization request instructionmay include the identifier of the descriptor. The step of, when adescriptor synchronization request instruction is received from a secondprocessor, determining a descriptor of tensor data to be synchronizedand the amount of data that can be synchronized among the tensor dataincludes:

parsing the descriptor synchronization request instruction to obtain theidentifier of the descriptor and the amount of data that can besynchronized; and

determining the descriptor of the tensor data to be synchronizedaccording to the identifier of the descriptor.

For example, if a descriptor indicating the tensor data to besynchronized are registered in the first processor and the secondprocessor, the descriptor synchronization instruction may only includethe identifier of the descriptor (for example, the descriptorsynchronization instruction may be represented as Send TR1 when theidentifier of the descriptor is TR1) and the amount of data that can besynchronized. The first processor may parse the descriptorsynchronization request instruction to obtain the identifier of thedescriptor and the amount of data that can be synchronized, and thendetermine the descriptor of the tensor data to be synchronized accordingto the identifier of the descriptor. In this way, the amount of datatransmitted during synchronization can be reduced, and the processingefficiency can be improved.

In some embodiments, the descriptor synchronization request instructionincludes the data characteristics of the tensor data to be synchronized.The step of, when a descriptor synchronization request instruction isreceived from a second processor, determining a descriptor of tensordata to be synchronized and the amount of data that can be synchronizedamong the tensor data includes:

parsing the descriptor synchronization request instruction to obtain thedata characteristics of the tensor data to be synchronized and theamount of data that can be synchronized; and

determining the descriptor of the tensor data according to the datacharacteristics of the tensor data.

For example, if the identifier of the descriptor already registered inthe first processor does not correspond to the identifier of thedescriptor of the tensor data determined in the descriptorsynchronization request instruction, the descriptor synchronizationinstruction may include the data characteristics of the tensor data tobe synchronized, where the data characteristics of the tensor data to besynchronized may include information such as the shape, source, andaddress of the tensor data. For example, the tensor data may be from aK-th sender (a K-th processor), the tensor data may be from an operationresult of a convolution operation numbered 200, the address of thetensor data may be a specific address area (for example, an addressADDR0-ADDR127), and the shape of the tensor data may be a specifiedshape (for example, the tensor data may be a 20*10 two-dimensionaltensor). Those skilled in the art can set the data characteristics ofthe tensor data to be synchronized according to the actual situation,which is not limited in the present disclosure.

In some embodiments, according to the data characteristics, the firstprocessor may determine the tensor data to be synchronized, anddetermine the descriptor of the tensor data to be synchronized, forexample, the first processor may directly obtain a descriptor orregister a corresponding descriptor. According to the descriptor of thetensor data to be synchronized, the tensor data may be determined, andthen the sub-data to be synchronized this time may be determinedaccording to the amount of data that can be synchronized.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the descriptor of the tensor data to besynchronized can be determined according to the data characteristics inthe descriptor synchronization request instruction, so as to achieve thesynchronization of part of the tensor data. In this way, there is noneed to transfer tensor data itself during synchronization, whichreduces the amount of transferred data and synchronization overhead, andimproves processing the efficiency.

In some embodiments, the step of determining first sub-data of thetensor data according to the descriptor of the tensor data and theamount of data that can be synchronized includes:

determining the tensor data and second sub-data in a to-be-synchronizedstate among the tensor data according to the descriptor of the tensordata; and

determining the first sub-data according to the second sub-data and theamount of data that can be synchronized.

For example, the state of data among the tensor data may be set, wherethe data that has been synchronized may be set to a synchronized state,and the data that has not been synchronized may be set to ato-be-synchronized state. In this case, when receiving the descriptorsynchronization request instruction from the second processor, the firstprocessor may determine the tensor data to be synchronized according tothe descriptor; according to the state of the data among the tensordata, the first processor may determine the second sub-data in theto-be-synchronized state; and according to the second sub-data and theamount of data that can be synchronized indicated by the descriptorsynchronization request instruction, the first processor may determinethe first sub-data to be synchronized this time.

In some embodiments, if the amount of the second sub-data is greaterthan the amount of data that can be synchronized, the first sub-datasynchronized this time can be selected from the second sub-data; if theamount of the second sub-data is less than or equal to the amount ofdata that can be synchronized, the second sub-data can be directly takenas the first sub-data.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, part of tensor data synchronized this time canbe determined, and then the part of tensor data can be synchronized,thereby improving the efficiency of data synchronization.

In some embodiments, the data synchronization method further includes:changing the state of the first sub-data of the tensor data from theto-be synchronized state to the synchronized state.

For example, after the first processor generates the descriptorsynchronization instruction according to the first sub-data of thetensor data and sends the descriptor synchronization instruction to thesecond processor to make the second processor synchronize the firstsub-data of the tensor data, the first processor can change the state ofdata among the tensor data, in other words, the first processor canchange the state of the first sub-data from the to-be synchronized stateto the synchronized state. In this way, when the synchronization staterequest of the second processor is received, data to be synchronizednext time can be determined from part of data in the to-be-synchronizedstate, thereby avoiding repeated data synchronization and improving datasynchronization efficiency.

In some embodiments, the present disclosure further provides a datasynchronization method applied to a second processor, including:

generating a descriptor synchronization request instruction according toa descriptor of tensor data to be synchronized and the amount of datathat can be synchronized among the tensor data, where the descriptor isused to indicate a shape of the tensor data to be synchronized, thedescriptor synchronization request instruction is used to instruct afirst processor to determine the descriptor of the tensor data to besynchronized and first sub-data of the tensor data according to thedescriptor synchronization request instruction, and the amount of thefirst sub-data corresponds to the amount of data that can besynchronized; and

sending the descriptor synchronization request instruction to the firstprocessor.

For example, the second processor among a plurality of processors may beset to be a receiver of data synchronization, and the second processormay issue the synchronization request for part of the tensor data. Inthe step S31 c, when there is tensor data to be synchronized in thesecond processor, the second processor can determine the descriptor ofthe tensor data and the amount of data that can be contained in thenon-shared storage space of the second processor allocated to the tensordata, i.e., the amount of data can be synchronized. According to thedescriptor of the tensor data and the amount of data that can besynchronized, the second processor may generate a descriptorsynchronization request instruction and send the instruction in the stepS32 c. The descriptor synchronization request instruction may include atleast one of an identifier of the descriptor, content of the descriptor,and data characteristic of the tensor data, and the descriptorsynchronization request instruction is used to instruct the firstprocessor to determine the descriptor of the tensor data to besynchronized and the first sub-data of the tensor data.

In some embodiments, when receiving the descriptor synchronizationrequest instruction, the first processor may parse the instruction todetermine the descriptor of the tensor data to be synchronized and theamount of data that can be synchronized; the first processor maydetermine the tensor data to be synchronized according to thedescriptor; and the first processor may determine the part of data thatcan be synchronized this time from the tensor data according to theamount of data that can be synchronized, i.e., the first sub-data. Theamount of the first sub-data may correspond to the amount of data thatcan be synchronized, for example, the amount of the first sub-data mayless than or equal to the amount of data that can be synchronized.

In some embodiments, if all the data of the tensor data has not beensynchronized, data that can be synchronized may be selected from thetensor data as the first sub-data. If part of the tensor data has notbeen synchronized, and the amount of data that has not been synchronizedis greater than the amount of data that can be synchronized, data thatcan be synchronized may be selected from the data that has not beensynchronized (second sub-data of the tensor data) as the first sub-data;if the amount of data that has not been synchronized is less than orequal to the amount of data that can be synchronized, the data that hasnot been synchronized can be directly taken as the first sub-data. Itshould be understood that those skilled in the art can determine thefirst sub-data according to the actual situation, which is not limitedin the present disclosure.

In some embodiments, the descriptor synchronization request instructionmay also include a range of part of tensor data to be synchronized, suchas the content of the descriptor of the part of sub-data or a range of astorage address of the part of sub-data, so as to determine the part ofdata to be synchronized.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the receiver can issue a synchronization requestfor part of the tensor data, so that the sender can determine thesub-data to be synchronized this time, thereby improving the efficiencyof data synchronization.

In some embodiments, the data synchronization method further includes:

when a descriptor synchronization instruction is received from a firstprocessor, determining a descriptor of tensor data to be synchronizedand first sub-data of the tensor data; and

storing the first sub-data of the tensor data according to thedescriptor of the tensor data.

For example, the first processor may generate and send a descriptorsynchronization instruction according to the descriptor of the tensordata and the first sub-data. When receiving the descriptorsynchronization instruction, the second processor may parse theinstruction to determine the descriptor of the tensor data to besynchronized and the first sub-data of the tensor data synchronized thistime; and then the second processor may determine the tensor data to besynchronized according to the descriptor, and store the first sub-dataof the tensor data in its own non-shared storage space.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the receiver can determine the descriptoraccording to the descriptor synchronization instruction and obtainsub-data synchronized this time, thereby reducing synchronizationoverhead and improving the efficiency of data synchronization.

In some embodiments, the identifier and content of the descriptor can bestored in the descriptor storage space, where the descriptor storagespace can be a storage space in an internal memory (such as a register,an on-chip SRAM, or other medium cache, etc.) of the processor. The datastorage space of the tensor data indicated by the descriptor may be astorage space in the internal memory (such as an on-chip cache) of theprocessor or a storage space in an external memory (an off-chip memory)connected to the processor. The data address in the data storage spacemay be an actual physical address or a virtual address. The presentdisclosure does not limit a position of the descriptor storage space anda position of the data storage space, and the type of the data address.

In some embodiments, the identifier and content of the descriptor, andtensor data indicated by the descriptor can be stored in a same area.For example, a continuous area of an on-chip cache with addressesADDR0-ADDR1023 can be used to store the above information. Within thisarea, addresses ADDR0-ADDR31 can be used to store the identifier of thedescriptor, addresses ADDR32-ADDR63 can be used to store the content ofthe descriptor, and addresses ADDR64-ADDR1023 can be used to store thetensor data indicated by the descriptor. The address ADDR is not limitedto 1 bit or 1 byte, and is an address unit used to represent an address.Those skilled in the art can determine the storage area and the addressthereof according to the specific applications, which is not limited inthe present disclosure.

In some embodiments, the identifier and content of the descriptor, andthe tensor data indicated by the descriptor can be respectively storedin different areas of an internal memory. For example, a register can beused as a descriptor storage space to store the identifier and contentof the descriptor, and an on-chip cache can be used as a data storagespace to store the tensor data indicated by the descriptor.

In some embodiments, a special register (SR) may be provided for thedescriptor, where data in the descriptor may be an immediate number orbe obtained from the special register. When the register is used tostore the identifier and content of the descriptor, a serial number ofthe register can be used to indicate the identifier of the descriptor.For example, if the serial number of the register is 0, the identifierof a descriptor stored in the register is 0. When the descriptor in theregister is valid, an area can be allocated in a caching space (such ascreating a tensor caching unit for each piece of tensor data in thecache) according to a size of tensor data indicated by the descriptorfor storing the tensor data. It should be understood that a presetcaching space may also be used to store the tensor data, which is notlimited in the present disclosure.

In some embodiments, the identifier and content of the descriptor can bestored in an internal memory, and the tensor data indicated by thedescriptor can be stored in an external memory. For example, theidentifier and content of the descriptor may be stored on-chip and thetensor data indicated by the descriptor may be stored off-chip.

In some embodiments, the data address of the data storage spacecorresponding to the descriptor may be a fixed address. For example, aseparate data storage space may be allocated for the tensor data, and astart address of each piece of tensor data in the data storage spacecorresponds to an identifier of the descriptor. In this case, theprocessor can determine the data address of the tensor data according tothe content of the descriptor.

In some embodiments, when the data address of the data storage spacecorresponding to the identifier of the descriptor is a variable address,the descriptor may be also used to indicate the address of N-dimensionaltensor data, where the content of the descriptor may further include atleast one address parameter representing the address of the tensor data.For example, if the tensor data is a 3-dimensional data, when thedescriptor points to the address of the tensor data, the content of thedescriptor may include an address parameter indicating the address ofthe tensor data, such as a start address of the tensor data; or thecontent of the descriptor may include a plurality of address parametersof the address of the tensor data, such as a start address+addressoffset of the tensor data, or address parameters of the tensor data ineach dimension. Those skilled in the art can set the address parametersaccording to actual needs, which is not limited in the presentdisclosure.

In some embodiments, the address parameter of the tensor data includes abase address of the datum point of the descriptor in the data storagespace of the tensor data, where the base address may vary from differentdatum points. The present disclosure does not limit the selection of thedatum point.

In some embodiments, the base address may include a start address of thedata storage space. When the datum point of the descriptor is a firstdata block of the data storage space, the base address of the descriptoris the start address of the data storage space. When the datum point ofthe descriptor is other data than the first data block in the datastorage space, the base address of the descriptor is the physicaladdress of the data block in the data storage space.

In some embodiments, the shape parameter of the tensor data includes atleast one of the followings: a size of the data storage space of thetensor data in at least one of N dimensions, a size of the storage areain at least one of the N dimensions, an offset of the storage area in atleast one of the N dimensions, a position of at least two vertices atdiagonal positions in the N dimensions relative to the datum point, anda mapping relationship between a data description position of the tensordata indicated by the descriptor and the data address of the tensor dataindicated by the descriptor. The data description position is a mappingposition of a point or an area in the tensor data indicated by thedescriptor, for example, if the tensor data is 3-dimensional data, thedescriptor can use a coordinate (x, y, z) to represent the shape of thetensor data, and the data description position of the tensor data can berepresented by the coordinate (x, y, z), and the data descriptionposition of the tensor data may be a position of a point or an areawhere the tensor data is mapped in a 3-dimensional space.

It should be understood that those skilled in the art may select a shapeparameter representing tensor data according to actual conditions, whichis not limited in the present disclosure.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, when the space of the receiver of datasynchronization is insufficient, part of tensor data can be synchronizedfirstly, and repeated many times until all of the tensor data aresynchronized, which can avoid the problems of overall synchronizationfailure or synchronization delay of tensor data in the case ofinsufficient space, and improve the efficiency of data synchronization.In addition, the descriptor indicating the shape of the tensor data isset, and the tensor data is determined according to the descriptorduring the data synchronization process, thereby reducingsynchronization overhead and reducing the complexity of data access.

It should be noted that, for the sake of simple description, the abovemethod embodiments are all described as a series of action combinations.However, those skilled in the art should be aware that the presentdisclosure is not limited by the described action order, becauseaccording to the present disclosure, certain steps may be executed inanother order or executed simultaneously. Those skilled in the artshould also be aware that the embodiments described in the specificationare alternative embodiments and that the actions and modules involvedare not necessary in the present disclosure.

It should be further noted that although the steps in the flow chartsare shown in sequence as indicated by the arrows, these steps are notnecessarily executed in the order indicated by the arrows. Unlessspecifically stated in the present disclosure, the execution of thesesteps is not strictly limited in order, and these steps may be executedin other orders. In addition, at least part of the steps in in the flowcharts may include a plurality of sub-steps or stages. These sub-stepsor stages are not necessarily executed at the same time, but may beexecuted at different times. The execution of these sub-steps or stagesis not necessarily performed sequentially, but may be performedalternately with other steps or at least a part of the sub-steps orstages of other steps.

FIG. 3c 2 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure. The datasynchronization apparatus is applied to a first processor. As shown inFIG. 3c 2, the data synchronization apparatus includes:

a query instruction generating module 51 c configured to, according to adescriptor of tensor data to be synchronized, generate a state queryinstruction, where the descriptor is used to indicate a shape of thetensor data to be synchronized, and the state query instruction is usedto instruct a second processor to determine the amount of tensor data tobe synchronized and generate a synchronization state instruction, wherethe state query instruction includes an identifier of the descriptorand/or content of the descriptor; and

a query instruction sending module 52 c configured to send the statequery instruction to the second processor.

In some embodiments, the data synchronization apparatus furtherincludes:

a sub-data determining module configured to, when the synchronizationstate instruction is received from the second processor, determine firstsub-data of the tensor data according to the descriptor of the tensordata in the synchronization state instruction and the amount of datathat can be synchronized, where the amount of the first sub-datacorresponds to the amount of data that can be synchronized; and

a synchronization instruction generating and sending module configuredto, according to the first sub-data, generate a descriptorsynchronization instruction and send the descriptor synchronizationinstruction to the second processor to instruct the second processor toobtain the first sub-data.

In some embodiments, the synchronization state instruction includes anidentifier of the descriptor, where the sub-data determining moduleincludes:

a parsing sub-module configured to parse the synchronization stateinstruction to obtain the identifier of the descriptor and the amount ofdata that can be synchronized; and

a descriptor determining sub-module configured to determine thedescriptor of the tensor data to be synchronized according to theidentifier of the descriptor.

In some embodiments, the sub-data determining module includes:

a first determining sub-module configured to determine the tensor dataand second sub-data to be synchronized among the tensor data accordingto the descriptor of the tensor data; and

a second determining sub-module configured to determine the firstsub-data according to the second sub-data and the amount of data thatcan be synchronized in the synchronization state instruction.

In some embodiments, the data synchronization apparatus furtherincludes:

a state changing module configured to change a state of the firstsub-data of the tensor data from a to-be synchronized state to asynchronized state.

FIG. 3c 3 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure. The datasynchronization apparatus is applied to a second processor. As shown inFIG. 3c 3, the data synchronization apparatus includes:

a query instruction receiving module 61 c configured to, when a statequery instruction is received from a first processor, determine adescriptor of tensor data to be synchronized, where the descriptor isused to indicate a shape of the tensor data to be synchronized;

a data amount determining module 62 c configured to, determine theamount of data that can be synchronized among the tensor data accordingto the descriptor of the tensor data;

a state instruction generating module 63 c configured to, according tothe descriptor of the tensor data and the amount of data that can besynchronized, generate a synchronization state instruction, where thesynchronization state instruction is used to instruct the firstprocessor to determine first sub-data of the tensor data, and the amountof the first sub-data corresponds to the amount of data that can besynchronized; and

a state instruction sending module 64 c configured to send thesynchronization state instruction to the first processor.

In some embodiments, the data synchronization apparatus furtherincludes:

a synchronization instruction receiving module configured to, when thedescriptor synchronization instruction is received from the firstprocessor, determine the descriptor of the tensor data to besynchronized and first sub-data of the tensor data; and

a data storing module configured to store the first sub-data of thetensor data according to the descriptor of the tensor data.

It should be understood that the apparatus embodiment described above isonly schematic, and the apparatus provided in the present disclosure maybe implemented in other manners. For example, division of theunits/modules is only logical function division and another divisionmanner may be adopted during practical implementation. For example, aplurality of units or components may be combined or integrated intoanother system or some characteristics may be neglected or notperformed.

In addition, unless otherwise specified, each functional unit/module inthe embodiments of the disclosure may be integrated into a unit/module,each unit/module may also physically exist independently, and two ormore units/modules may also be integrated into one unit/module. Theintegrated unit/module may be implemented in the form of hardware or asoftware functional unit/module.

If the integrated unit/module is implemented in the form of hardware,the hardware may be a digital circuit, an analogue circuit, and thelike. The physical implementation of hardware may include, but is notlimited to, a transistor, a memristor, and the like. Unless otherwisespecified, the artificial intelligence processor may be any appropriatehardware processor, such as CPU, GPU, FPGA, DSP, ASIC, and the like.Unless otherwise specified, the storage unit may be any proper magneticstorage medium or magneto-optic storage medium, for example, an RRAM(Resistive Random Access Memory), a DRAM (Dynamic Random Access Memory),an SRAM (Static Random-Access Memory), an EDRAM (Enhanced Dynamic RandomAccess Memory), an HBM (High-Bandwidth Memory), an HMC (Hybrid MemoryCube), and the like.

If being implemented in the form of a software program module and soldor used as an independent product, the integrated unit/module may bestored in a computer-readable memory. Based on such an understanding,all or part of the technical solutions may be embodied in form ofsoftware product. The computer software product is stored in a memory,including a plurality of instructions configured to enable a computerdevice (which may be a PC, a server, a network device, or the like) toperform all or part of the operations of the method in each embodimentof the application. The memory may include various media capable ofstoring program codes such as a U disk, a mobile hard disk, a read-onlymemory (ROM), a random access memory (RAM), a magnetic disk, or anoptical disk.

In some embodiments, the present disclosure provides an artificialintelligence chip including the above-mentioned data synchronizationapparatus.

In some embodiments, the present disclosure provides a board cardincluding a storage device, an interface apparatus, a control device,and the above-mentioned artificial intelligence chip. The artificialintelligence chip is connected to the storage device, the controldevice, and the interface apparatus, respectively; the storage device isconfigured to store data; the interface apparatus is configured toimplement data transfer between the artificial intelligence chip and anexternal equipment; and the control device is configured to monitor astate of the artificial intelligence chip.

In the above-mentioned embodiments, the description of each embodimenthas its own focus. For parts that are not described in detail in anembodiment, please refer to related descriptions of other embodiments.The technical features of the above-mentioned embodiments may becombined arbitrarily. In order to make the description concise, not allpossible combinations of the various technical features in theabove-mentioned embodiments are described. However, as long as there isno contradiction in the combinations of these technical features, theyshould be regarded as the scope of this specification.

The foregoing may be better understood according to the followingarticles:

A1. A data synchronization method applied to a first processor,comprising:

according to a descriptor of tensor data to be synchronized, generatinga state query instruction, wherein the descriptor is used to indicate ashape of the tensor data to be synchronized, and the state queryinstruction is used to instruct a second processor to determine theamount of tensor data to be synchronized and generate a synchronizationstate instruction, wherein the state query instruction includes anidentifier of the descriptor and/or content of the descriptor; and

sending the state query instruction to the second processor.

A2. The data synchronization method of A1, further comprising:

when the synchronization state instruction is received from the secondprocessor, determining first sub-data of the tensor data according tothe descriptor of the tensor data in the synchronization stateinstruction and the amount of data that can be synchronized, wherein theamount of the first sub-data corresponds to the amount of data that canbe synchronized; and

according to the first sub-data, generating a descriptor synchronizationinstruction and sending the descriptor synchronization instruction tothe second processor to instruct the second processor to obtain thefirst sub-data.

A3. The data synchronization method of A2, wherein the synchronizationstate instruction includes the identifier of the descriptor, wherein thedetermining first sub-data of the tensor data according to thedescriptor of the tensor data in the synchronization state instructionand the amount of data that can be synchronized when the synchronizationstate instruction is received from the second processor includes:

parsing the synchronization state instruction to obtain the identifierof the descriptor and the amount of data that can be synchronized; and

according to the identifier of the descriptor, determining thedescriptor of the tensor data to be synchronized.

A4. The data synchronization method of A2 or A3, wherein the determiningfirst sub-data of the tensor data according to the descriptor of thetensor data in the synchronization state instruction and the amount ofdata that can be synchronized when the synchronization state instructionis received from the second processor includes:

determining the tensor data and second sub-data to be synchronized amongthe tensor data according to the descriptor of the tensor data; and

determining the first sub-data according to the second sub-data and theamount of data that can be synchronized in the synchronization stateinstruction.

A5. The data synchronization method of any one of A2-A4, furthercomprising:

changing a state of the first sub-data of the tensor data from a to-besynchronized state to a synchronized state.

A6. A data synchronization method applied to a second processor,comprising:

when a state query instruction is received from a first processor,determining a descriptor of tensor data to be synchronized, wherein thedescriptor is used to indicate a shape of the tensor data to besynchronized;

determining the amount of data that can be synchronized among the tensordata according to the descriptor of the tensor data;

according to the descriptor of the tensor data and the amount of datathat can be synchronized, generating a synchronization stateinstruction, wherein the synchronization state instruction is used toinstruct the first processor to determine first sub-data of the tensordata, and the amount of the first sub-data corresponds to the amount ofdata that can be synchronized; and

sending the synchronization state instruction to the first processor.

A7. The data synchronization method of A6, further comprising:

when the descriptor synchronization instruction is received from thefirst processor, determining the descriptor of the tensor data to besynchronized and first sub-data of the tensor data; and

according to the descriptor of the tensor data, storing the firstsub-data of the tensor data.

A8. A data synchronization apparatus applied to a first processor,comprising:

a query instruction generating module configured to, according to adescriptor of tensor data to be synchronized, generate a state queryinstruction, wherein the descriptor is used to indicate a shape of thetensor data to be synchronized, and the state query instruction is usedto instruct a second processor to determine the amount of tensor data tobe synchronized and generate a synchronization state instruction,wherein the state query instruction includes an identifier of thedescriptor and/or content of the descriptor; and

a query instruction sending module configured to send the state queryinstruction to the second processor.

A9. The data synchronization apparatus of A8, further comprising:

a sub-data determining module configured to, when the synchronizationstate instruction is received from the second processor, determine firstsub-data of the tensor data according to the descriptor of the tensordata in the synchronization state instruction and the amount of datathat can be synchronized, wherein the amount of the first sub-datacorresponds to the amount of data that can be synchronized; and

a synchronization instruction generating and sending module configuredto, according to the first sub-data, generate a descriptorsynchronization instruction and send the descriptor synchronizationinstruction to the second processor to instruct the second processor toobtain the first sub-data.

A10. The data synchronization apparatus of A9, wherein thesynchronization state instruction includes an identifier of thedescriptor, wherein the sub-data determining module includes:

a parsing sub-module configured to parse the synchronization stateinstruction to obtain the identifier of the descriptor and the amount ofdata that can be synchronized; and

a descriptor determining sub-module configured to determine thedescriptor of the tensor data to be synchronized according to theidentifier of the descriptor.

A11. The data synchronization apparatus of A9 or A10, wherein thesub-data determining module includes:

a first determining sub-module configured to determine the tensor dataand second sub-data to be synchronized among the tensor data accordingto the descriptor of the tensor data; and

a second determining sub-module configured to determine the firstsub-data according to the second sub-data and the amount of data thatcan be synchronized in the synchronization state instruction.

A12. The data synchronization apparatus of any one of A9-A11, furthercomprising:

a state changing module configured to change a state of the firstsub-data of the tensor data from a to-be synchronized state to asynchronized state.

A13. A data synchronization apparatus applied to a second processor,comprising:

a query instruction receiving module configured to, when a state queryinstruction is received from a first processor, determine a descriptorof tensor data to be synchronized, wherein the descriptor is used toindicate a shape of the tensor data to be synchronized;

a determining module of the amount of data configured to, determine theamount of data that can be synchronized among the tensor data accordingto the descriptor of the tensor data; a state instruction generatingmodule configured to, according to the descriptor of the tensor data andthe amount of data that can be synchronized, generate a synchronizationstate instruction, wherein the synchronization state instruction is usedto instruct the first processor to determine first sub-data of thetensor data, and the amount of the first sub-data corresponds to theamount of data that can be synchronized; and

a state instruction sending module configured to send thesynchronization state instruction to the first processor.

A14. The data synchronization apparatus of A13, further comprising:

a synchronization instruction receiving module configured to, when thedescriptor synchronization instruction is received from the firstprocessor, determine the descriptor of the tensor data to besynchronized and first sub-data of the tensor data; and

a data storing module configured to store the first sub-data of thetensor data according to the descriptor of the tensor data.

A15. An artificial intelligence chip comprising the data synchronizationapparatus of any one of A8-A14.

A16. An electronic device comprising the artificial intelligence chip ofA15.

A17. A board card comprising a storage device, an interface apparatus, acontrol device, and the artificial intelligence chip of A15, wherein

the artificial intelligence chip is connected to the storage device, thecontrol device, and the interface apparatus, respectively;

the storage device is configured to store data;

the interface apparatus is configured to implement data transfer betweenthe artificial intelligence chip and an external equipment; and

the control device is configured to monitor a state of the artificialintelligence chip.

A18. The board card of A17, wherein

the storage device includes a plurality of groups of storage units,wherein each group of the storage units is connected with the artificialintelligence chip by a bus, and the storage units are DDR SDRAMs;

the chip includes a DDR controller configured to control data transferand data storage of each storage unit; and

the interface apparatus is a standard PCIE interface.

With the continuous development of the A1 (Artificial Intelligence)technology, it has gradually obtained wide application and worked wellin the fields of image recognition, speech recognition, and naturallanguage processing, and the like. However, as the complexity of A1algorithms is growing, the amount of data and data dimensions that needto be processed are increasing, therefore, multi-core and/or multi-chipdata are usually required for data processing. When data is synchronizedbetween cores or chips, a synchronization method adopting therelated-art may result in large synchronization overhead and lowprocessing efficiency.

In some embodiments, the present disclosure provides a datasynchronization method.

FIG. 1f shows a flowchart of a data synchronization method according toan embodiment of the present disclosure. As shown in FIG. 1f , the datasynchronization method is applied to a first processor (any processor ina processing system), and the method includes:

a step S11 f: according to a descriptor of tensor data to besynchronized, determining data characteristics of the tensor data, wherethe descriptor is used to indicate a shape of the tensor data to besynchronized; and

a step S12 f: according to the data characteristics of the tensor data,generating a state query instruction and sending the state queryinstruction to a second processor, where the state query instruction isused to instruct the second processor to determine the amount of tensordata to be synchronized and generate a synchronization stateinstruction.

For example, the data to be synchronized may include N-dimensionaltensor data (N is an integer greater than or equal to 0, for example,N=1, 2, or 3).

In some embodiments, during data processing, data synchronizationbetween a plurality of processors (such as a plurality of cores of anartificial intelligence chip) may be executed, for example, an operationresult of a processor A1 may be synchronized to a processor A2 as inputdata of another operation. In this case, a data synchronizationmechanism based on the descriptor can be used to achieve datasynchronization.

In some embodiments, since a non-shared storage space of each processorallocated to the tensor data to be synchronized may be limited, thetensor data cannot be synchronized at the same time. In this case, partof tensor data can be synchronized firstly, and repeated many timesuntil all of the tensor data are synchronized.

In some embodiments, when there is tensor data to be synchronized in asender of data to be synchronized, for example, when an operation iscompleted and an operation result (tensor data) is obtained, the sendercan query the state of the receiver to determine the amount of data thatcan be contained in the non-shared storage space of the receiverallocated to the tensor data, so that part of tensor data can besynchronized.

In some embodiments, the first processor among a plurality of processorsmay be set as the sender of data to be synchronized, and the secondprocessor may be set as the receiver of data synchronization. Both thefirst processor and the second processor are any of the plurality ofprocessors, and the second processor may be of the same type ordifferent from the first processor. The present disclosure does notlimit the type of the first processor and the type of the secondprocessor.

In some embodiments, when the first processor determines that there istensor data to be synchronized, the first processor may obtain thedescriptor of the tensor data. The descriptor may be a registered(created) descriptor indicating the shape of the tensor data, or a newdescriptor registered (created) according to the shape parameter of thetensor data, which is not limited in the present disclosure.

In some embodiments, in the step S11 f, according to the descriptor ofthe tensor data, the first processor may determine the datacharacteristics of the tensor data. The data characteristics may includeat least one of the identifier (for example, a serial number of data),shape, source, and storage address of the tensor data.

In some embodiments, the data characteristics of the tensor data to besynchronized may include information such as the shape, source, andaddress of the tensor data. For example, the tensor data may be from aK-th sender (a K-th processor), the tensor data may be from an operationresult of a convolution operation numbered 200, the address of thetensor data may be a specific address area (for example, an addressADDR1-ADDR127), and the shape of the tensor data may be a specifiedshape (for example, the tensor data may be a 20*10 two-dimensionaltensor). Those skilled in the art can set the data characteristics ofthe tensor data to be synchronized according to the actual situation,which is not limited in the present disclosure.

In some embodiments, in the step S12 f, according to the datacharacteristics of the tensor data, the first processor may generate astate query instruction and send the state query instruction to thesecond processor. If the second processor already has information (forexample, a descriptor indicating the tensor data to be synchronized hasbeen registered) of the tensor data, the state query instruction mayonly include part of the data characteristics, such as the identifier ofthe tensor data, and then the state query instruction may instruct thesecond processor to determine the descriptor of the tensor data to besynchronized according to the identifier of the tensor data; if thesecond processor does not have information of the tensor data, thesynchronization instruction can include more data characteristics, suchas the identifier and storage address of the tensor data, and then thesynchronization instruction may instruct the second processor todetermine the descriptor of the tensor data to be synchronized. Thepresent disclosure does not limit the specific content included in thestate query instruction.

In some embodiments, if the state query instruction includes theidentifier of the tensor data, the second processor may determine thetensor data to be synchronized according to the identifier, and registeror obtain the descriptor indicating the tensor data to be synchronized.If the state query instruction includes more data characteristics (theidentifier and storage address, etc.), the second processor can registerthe descriptor indicating the tensor data to be synchronized accordingto the data characteristics in the instruction.

In some embodiments, after determining the descriptor of the tensor datato be synchronized, the second processor may determine the space thatcan be allocated to the tensor data corresponding to the descriptor, anddetermine the amount of data that can be synchronized among the tensordata According to the amount of data that can be synchronized and thedata characteristics, the second processor can generate and send asynchronization state instruction, so that the first processor candetermine the tensor data to be synchronized and the amount of data thatcan be synchronized this time.

According to the above-mentioned data synchronization method provided bythe present disclosure, by setting the descriptor indicating the shapeof the tensor data, the sender can determine the data characteristics ofthe tensor data according to the descriptor, and generate and send thestate query instruction according to the data characteristics toinstruct the receiver to feedback its own state (i.e., the amount ofdata that can be synchronized) according to the state query instruction,therefore, part of tensor data can be synchronized, the synchronizationoverhead can be reduced without changing the instruction structure, andthe efficiency of data synchronization can be improved.

In some embodiments, the data synchronization method further includes:

when the synchronization state instruction is received from the secondprocessor, parsing the synchronization state instruction to determinethe data characteristics of the tensor data to be synchronized and theamount of data that can be synchronized;

determining the descriptor of the tensor data to be synchronizedaccording to the data characteristics;

determining first sub-data of the tensor data according to thedescriptor and the amount of data that can be synchronized, where theamount of the first sub-data corresponds to the amount of data that canbe synchronized; and

according to the first sub-data, generating a synchronizationinstruction and sending the synchronization instruction to the secondprocessor to instruct the second processor to obtain the first sub-data.

For example, when receiving the synchronization state instruction fromthe second processor, the first processor may parse the instruction toobtain content of the instruction, i.e., the data characteristics of thetensor data to be synchronized and the amount of data that can besynchronized. According to the data characteristics, the descriptor ofthe tensor data to be synchronized can be determined, and then thetensor data to be synchronized can be determined; and the part of datathat can be synchronized this time (the first sub-data) is determinedfrom the tensor data according to the amount of data that can besynchronized. The amount of the first sub-data may correspond to theamount of data that can be synchronized, for example, the amount of thefirst sub-data may be less than or equal to the amount of data that canbe synchronized.

In some embodiments, if all the data of the tensor data has not beensynchronized, data that can be synchronized may be selected from thetensor data as the first sub-data. If part of the tensor data has notbeen synchronized, and the amount of data that has not been synchronizedis greater than the amount of data that can be synchronized, data thatcan be synchronized may be selected from the data that has not beensynchronized (second sub-data of the tensor data) as the first sub-data;if the amount of data that has not been synchronized is less than orequal to the amount of data that can be synchronized, the data that hasnot been synchronized can be directly taken as the first sub-data. Itshould be understood that those skilled in the art can determine thefirst sub-data according to the actual situation, which is not limitedin the present disclosure.

In some embodiments, the synchronization state instruction may alsoinclude a range of part of tensor data to be synchronized, such as arange of a storage address of the part of sub-data, so as to determinethe part of data to be synchronized. The first processor may directlydetermine the first sub-data to be synchronized according to the rangeof the part of data.

In some embodiments, the first processor may generate a synchronizationinstruction according to the first sub-data and send the synchronizationinstruction to the second processor. The synchronization instruction mayinclude the data characteristics of the tensor data to be synchronizedand the first sub-data. After receiving the synchronization instruction,the second processor may parse the synchronization instruction todetermine the data characteristics of the tensor data to be synchronizedand the first sub-data of the tensor data, determine the descriptoraccording to the data characteristics, and store the first sub-data ofthe tensor data in its own non-shared storage space.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the descriptor of the tensor data and the amountof data that can be synchronized can be determined according to thesynchronization state instruction from the sender; the sub-datasynchronized this time can be determined according to the amount of datathat can be synchronized; and the synchronization instruction can begenerated and sent according to the sub-data, so that the receiver canobtain the sub-data synchronized this time, thereby reducing thesynchronization overhead and improving the efficiency of datasynchronization.

In some embodiments, the step of determining the first sub-data of thetensor data according to the descriptor and the amount of data that canbe synchronized includes:

according to the descriptor, determining the tensor data to besynchronized and second sub-data in a to-be-synchronized state among thetensor data; and

determining the first sub-data according to the second sub-data and theamount of data that can be synchronized.

For example, the state of data among the tensor data may be set, wherethe data that has been synchronized may be set to a synchronized state,and the data that has not been synchronized may be set to ato-be-synchronized state. In this case, when receiving thesynchronization state instruction from the second processor, the firstprocessor may determine the second sub-data in the to-be-synchronizedstate according to the state of the data among the tensor data; andaccording to the second sub-data and the amount of data that can besynchronized indicated by the synchronization state instruction, thefirst processor may determine the first sub-data to be synchronized thistime.

In some embodiments, if the amount of the second sub-data is greaterthan the amount of data that can be synchronized, the first sub-datasynchronized this time can be selected from the second sub-data; if theamount of the second sub-data is less than or equal to the amount ofdata that can be synchronized, the second sub-data can be directly takenas the first sub-data.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, part of tensor data synchronized this time canbe determined, and then the part of tensor data can be synchronized,thereby improving the efficiency of data synchronization.

In some embodiments, the data synchronization method further includes:changing a state of the first sub-data of the tensor data from a to-besynchronized state to a synchronized state.

For example, after the first processor generates the synchronizationinstruction according to the first sub-data of the tensor data and sendsthe descriptor synchronization instruction to the second processor tomake the second processor synchronize the first sub-data of the tensordata, the first processor can change the state of data among the tensordata, in other words, the first processor can change the state of thefirst sub-data from the to-be synchronized state to the synchronizedstate. In this way, when the state of the second processor is queriednext time and the synchronization state instruction of the secondprocessor is received, data to be synchronized next time can bedetermined from part of data in the to-be-synchronized state, therebyavoiding repeated data synchronization and improving datasynchronization efficiency.

FIG. 3d 1 shows a flowchart of a data synchronization method accordingto an embodiment of the present disclosure. As shown in FIG. 3d 1, thedata synchronization method is applied to a second processor, and themethod includes:

a step S31 d: when a state query instruction is received from a firstprocessor, parsing the state query instruction to obtain datacharacteristics of tensor data to be synchronized;

a step S32 d: determining a descriptor of the tensor data to besynchronized according to the data characteristics, where the descriptoris used to indicate a shape of the tensor data to be synchronized;

a step S33 d: determining the amount of data that can be synchronizedamong the tensor data according to the descriptor of the tensor data;and

a step S34 d: according to the data characteristics of the tensor dataand the amount of data that can be synchronized, generating asynchronization state instruction and sending the synchronization stateinstruction to the first processor, where the synchronization stateinstruction is used to instruct the first processor to determine firstsub-data of the tensor data, and the amount of the first sub-datacorresponds to the amount of data that can be synchronized.

For example, when there is tensor data to be synchronized in a sender ofdata to be synchronized, the sender may query the state of the receiver.The first processor (sender) may generate and send the state queryinstruction, and when the second processor receives the state queryinstruction in step S31 d, the second processor may parse the statequery instruction to determine the data characteristics of the tensordata to be synchronized, where the data characteristics may include atleast one of the identifier (for example, a serial number of data),shape, source, and storage address of the tensor data.

In some embodiments, in the step S32 d, the second processor maydetermine the descriptor of the tensor data to be synchronized accordingto the data characteristics. The descriptor may be a registered(created) descriptor indicating the shape of the tensor data, or a newdescriptor registered (created) according to the shape parameter of thetensor data, which is not limited in the present disclosure.

In some embodiments, in the step S33 d, the second processor maydetermine the tensor data to be synchronized according to thedescriptor, and determine the amount of data that can be contained inthe non-shared storage space of the second processor allocated to thetensor data, i.e., the amount of data that can be synchronized, so thatpart of tensor data can be synchronized.

In some embodiments, in the step S34 d, the second processor maygenerate and send a synchronization state instruction to the firstprocessor according to the determined amount of data that can besynchronized and the data characteristics of the tensor data, so as toinstruct the first processor to determine the amount of data that can besynchronized this time. After determining the part of data (i.e., thefirst sub-data) that can be synchronized this time, the first processormay generate a synchronization instruction and send the synchronizationinstruction to the second processor. The synchronization instruction mayinclude the data characteristics of the tensor data to be synchronizedand the first sub-data of the tensor data.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the sender may query the state of the receiver;after receiving the state query instruction, the receiver determines andresponds to its own state (i.e., the amount of data that can besynchronized). In this way, part of tensor data can be synchronizedthrough interaction, which may improve the efficiency of datasynchronization.

In some embodiments, the data synchronization method further includes:

when a synchronization instruction is received from the first processor,parsing the synchronization instruction to obtain the datacharacteristics of the tensor data to be synchronized and the firstsub-data of the tensor data;

determining the descriptor of the tensor data according to the datacharacteristics; and

storing the first sub-data of the tensor data according to thedescriptor of the tensor data.

For example, when receiving the synchronization instruction, the secondprocessor may parse the instruction to determine the datacharacteristics of the tensor data to be synchronized and the firstsub-data of the tensor data to be synchronized this time; according tothe data characteristics, the second processor may determine thedescriptor of the tensor data to be synchronized, and according to thedescriptor, the second processor may determine the tensor data to besynchronized, and then store the first sub-data of the tensor data inits own non-shared storage space.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the receiver can determine the descriptoraccording to the synchronization instruction and obtain sub-datasynchronized this time, thereby reducing synchronization overhead andimproving the efficiency of data synchronization.

In some embodiments, the receiver of data synchronization can issue asynchronization request for part of tensor data, in other words, thereceiver sends a descriptor synchronization request instruction, wherethe descriptor synchronization request instruction may determine thedescriptor of the tensor data to be synchronized and the amount of datathat can be synchronized among the tensor data, i.e., the amount of datathat can be contained in the non-shared storage space of the receiverallocated to the tensor data.

In some embodiments, the present disclosure provides a datasynchronization method applied to a first processor, including:

when a synchronization request instruction is received from a secondprocessor, parsing the synchronization request instruction to obtaindata characteristics of tensor data to be synchronized and the amountdata that can be synchronized among the tensor data;

determining a descriptor of the tensor data to be synchronized accordingto the data characteristics, where the descriptor is used to indicate ashape of the tensor data to be synchronized:

determining first sub-data of the tensor data according to thedescriptor of the tensor data and the amount of data that can besynchronized, where the amount of the first sub-data corresponds to theamount of data that can be synchronized; and

according to the first sub-data, generating a synchronizationinstruction and sending the synchronization instruction to the secondprocessor to instruct the second processor to obtain the first sub-data.

For example, the receiver of data synchronization can issue asynchronization request for part of tensor data, in other words, thereceiver sends a synchronization request instruction, where thedescriptor synchronization request instruction may determine the datacharacteristics of the tensor data to be synchronized and the amount ofdata that can be synchronized among the tensor data, i.e., the amount ofdata that can be contained in the non-shared storage space of thereceiver allocated to the tensor data.

In some embodiments, the first processor among a plurality of processorsmay be set as the sender of data to be synchronized, and the secondprocessor may be set as the receiver of data synchronization. Both thefirst processor and the second processor are any of the plurality ofprocessors, and the second processor may be of the same type ordifferent from the first processor. The present disclosure does notlimit the type of the first processor and the type of the secondprocessor.

In some embodiments, when receiving the synchronization requestinstruction from the second processor, the first processor may parse theinstruction to obtain content of the instruction, i.e., the datacharacteristics of the tensor data to be synchronized and the amount ofdata that can be synchronized, where the data characteristics mayinclude at least one of the identifier (for example, a serial number ofdata), shape, source, and storage address of the tensor data.

In some embodiments, the data characteristics of the tensor data to besynchronized may include information such as the shape, source, andaddress of the tensor data. For example, the tensor data may be from aK-th sender (a K-th processor), the tensor data may be from an operationresult of a convolution operation numbered 200, the address of thetensor data may be a specific address area (for example, an addressADDR0-ADDR127), and the shape of the tensor data may be a specifiedshape (for example, the tensor data may be a 20*10 two-dimensionaltensor). Those skilled in the art can set the data characteristics ofthe tensor data to be synchronized according to the actual situation,which is not limited in the present disclosure.

In some embodiments, the first processor may determine the descriptor ofthe tensor data to be synchronized according to the datacharacteristics, and determine the tensor data to be synchronizedaccording to the descriptor, and then determine the part of data thatcan be synchronized this time from the tensor data according to theamount of data that can be synchronized, i.e., the first sub-data. Theamount of the first sub-data may correspond to the amount of data thatcan be synchronized, for example, the amount of the first sub-data maybe less than or equal to the amount of data that can be synchronized.

In some embodiments, if all the data of the tensor data has not beensynchronized, data that can be synchronized may be selected from thetensor data as the first sub-data. If part of the tensor data has notbeen synchronized, and the amount of data that has not been synchronizedis greater than the amount of data that can be synchronized, data thatcan be synchronized may be selected from the data that has not beensynchronized (second sub-data of the tensor data) as the first sub-data;if the amount of data that has not been synchronized is less than orequal to the amount of data that can be synchronized, the data that hasnot been synchronized can be directly taken as the first sub-data. Itshould be understood that those skilled in the art can determine thefirst sub-data according to the actual situation, which is not limitedin the present disclosure.

In some embodiments, the synchronization request instruction may alsoinclude a range of part of tensor data to be synchronized, such as arange of a storage address of the part of sub-data, so as to determinethe part of data to be synchronized. The first processor may directlydetermine the first sub-data to be synchronized according to the rangeof the part of data.

In some embodiments, the first processor may generate a synchronizationinstruction according to the first sub-data and send the synchronizationinstruction to the second processor. The synchronization instruction mayinclude the data characteristics of the tensor data to be synchronizedand the first sub-data. After receiving the synchronization instruction,the second processor may parse the synchronization instruction todetermine the data characteristics and the first sub-data, determine thedescriptor according to the data characteristics, and determine thetensor data to be synchronized according to the descriptor, and thenstore the first sub-data of the tensor data in its own non-sharedstorage space.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the receiver may issue a synchronization requestinstruction to actively request the synchronization of part of the data,and the sender may determine the sub-data synchronized this timeaccording to the amount of data that can be synchronized received fromthe receiver. According to the sub-data, the synchronization instructionmay be generated and sent, so that the receiver can obtain the sub-datasynchronized this time, thereby reducing the synchronization overheadwithout changing the instruction structure and improving the efficiencyof data synchronization.

In some embodiments, the step of determining first sub-data of thetensor data according to the descriptor of the tensor data and theamount of data that can be synchronized includes:

determining the tensor data and second sub-data in a to-be-synchronizedstate among the tensor data according to the descriptor of the tensordata; and

determining the first sub-data according to the second sub-data and theamount of data that can be synchronized.

For example, the state of data among the tensor data may be set, wherethe data that has been synchronized may be set to a synchronized state,and the data that has not been synchronized may be set to ato-be-synchronized state. In this case, when receiving thesynchronization request instruction from the second processor, the firstprocessor may determine the second sub-data in the to-be-synchronizedstate according to the state of the data among the tensor data; andaccording to the second sub-data and the amount of data that can besynchronized indicated by the synchronization state instruction, thefirst processor may determine the first sub-data to be synchronized thistime.

In some embodiments, if the amount of the second sub-data is greaterthan the amount of data that can be synchronized, the first sub-datasynchronized this time can be selected from the second sub-data; if theamount of the second sub-data is less than or equal to the amount ofdata that can be synchronized, the second sub-data can be directly takenas the first sub-data.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, part of tensor data synchronized this time canbe determined, and then the part of tensor data can be synchronized,thereby improving the efficiency of data synchronization.

In some embodiments, the data synchronization method further includes:changing a state of the first sub-data of the tensor data from a to-besynchronized state to a synchronized state.

For example, after the first processor generates and sends thesynchronization instruction according to the first sub-data of thetensor data, so that the second processor can synchronize the firstsub-data of the tensor data, the first processor may change a state ofdata among the tensor data, in other words, the first processor canchange the state of the first sub-data from the to-be synchronized stateto the synchronized state. In this way, when receiving thesynchronization request instruction from the second processor next time,the first processor can determine data to be synchronized next time frompart of data in the to-be-synchronized state, thereby avoiding repeateddata synchronization and improving data synchronization efficiency.

In some embodiments, the present disclosure provides a datasynchronization method applied to a second processor, including:

according to a descriptor of tensor data to be synchronized, determiningdata characteristics of the tensor data and the amount of data that canbe synchronized among the tensor data, where the descriptor is used toindicate a shape of the tensor data to be synchronized; and

according to the data characteristics of the tensor data and the amountof data that can be synchronized, generating a synchronization requestinstruction and sending the synchronization request instruction to thefirst processor, where the synchronization request instruction is usedto instruct the first processor to determine the tensor data to besynchronized and first sub-data of the tensor data according to thesynchronization request instruction, and the amount of the firstsub-data corresponds to the amount of data that can be synchronized.

For example, a receiver of data to be synchronized (i.e., the secondprocessor) may issue a synchronization request for part of the tensordata. When there is tensor data to be synchronized in the secondprocessor, the descriptor of the tensor data can be determined. Thedescriptor may be a registered (created) descriptor indicating the shapeof the tensor data, or a new descriptor registered (created) accordingto the shape parameter of the tensor data, which is not limited in thepresent disclosure.

In some embodiments, the second processor may determine the datacharacteristics of the tensor data according to the descriptor, wherethe data characteristics may include at least one of the identifier (forexample, a serial number of data), shape, source, and storage address ofthe tensor data. In addition, the second processor can determine theamount of data that can be contained in the non-shared storage space ofthe second processor allocated to the tensor data, i.e., the amount ofdata can be synchronized.

In some embodiments, according to the data characteristics of the tensordata and the amount of data that can be synchronized, the secondprocessor may generate a synchronization request instruction and sendthe instruction, where the synchronization request instruction is usedto instruct the first processor to determine the tensor data to besynchronized and the first sub-data of the tensor data.

In some embodiments, when receiving the synchronization requestinstruction, a receiver of data to be synchronized (i.e., the firstprocessor) may parse the instruction to determine the datacharacteristics of the tensor data to be synchronized and the amount ofdata that can be synchronized, according to the data characteristics,the receiver may determine the descriptor of the tensor data to besynchronized; according to the descriptor, the receiver may determinethe tensor data to be synchronized, and determine the part of data thatcan be synchronized this time among the tensor data according to theamount of data that can be synchronized, i.e., the first sub-data. Theamount of the first sub-data may correspond to the amount of data thatcan be synchronized, for example, the amount of the first sub-data maybe less than or equal to the amount of data that can be synchronized.

In some embodiments, if all the data of the tensor data has not beensynchronized, data that can be synchronized may be selected from thetensor data as the first sub-data. If part of the tensor data has notbeen synchronized, and the amount of data that has not been synchronizedis greater than the amount of data that can be synchronized, data thatcan be synchronized may be selected from the data that has not beensynchronized (second sub-data of the tensor data) as the first sub-data;if the amount of data that has not been synchronized is less than orequal to the amount of data that can be synchronized, the data that hasnot been synchronized can be directly taken as the first sub-data. Itshould be understood that those skilled in the art can determine thefirst sub-data according to the actual situation, which is not limitedin the present disclosure.

In some embodiments, the synchronization request instruction may alsoinclude a range of part of tensor data to be synchronized, such ascontent of the descriptor of the part of sub-data and a range of astorage address of the part of sub-data, so as to determine the part ofdata to be synchronized.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the receiver may issue a synchronization requestfor part of the tensor data, so that the sender can determine thesub-data synchronized this time, thereby improving the efficiency ofdata synchronization.

In some embodiments, the data synchronization method further includes:

when a synchronization instruction is received from a first processor,parsing the synchronization instruction to obtain data characteristicsof tensor data to be synchronized and first sub-data of the tensor data;

determining the descriptor of the tensor data according to the datacharacteristics; and

storing the first sub-data of the tensor data according to thedescriptor of the tensor data.

For example, the first processor may generate and send thesynchronization instruction according to the data characteristics of thetensor data and the first sub-data. When receiving the synchronizationinstruction, the second processor may parse the instruction to determinethe data characteristics of the tensor data to be synchronized and thefirst sub-data of the tensor data synchronized this time; according tothe data characteristics, the second processor may determine thedescriptor, and then determine the tensor data to be synchronizedaccording to the descriptor; and the second processor may store thefirst sub-data of the tensor data in its own non-shared storage space.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, according to the synchronization instruction,the receiver can determine the descriptor and obtain the sub-datasynchronized this time, thereby reducing the synchronization overhead,improving the efficiency of data synchronization, and achievinginstruction compatibility during instruction transfer and processing.

In some embodiments, the identifier and content of the descriptor can bestored in the descriptor storage space, where the descriptor storagespace can be a storage space in an internal memory (such as a register,an on-chip SRAM, or other medium cache, etc.) of the processor. The datastorage space of the tensor data indicated by the descriptor may be astorage space in the internal memory (such as an on-chip cache) of theprocessor or a storage space in an external memory (an off-chip memory)connected to the processor. The data address in the data storage spacemay be an actual physical address or a virtual address. The presentdisclosure does not limit a position of the descriptor storage space anda position of the data storage space, and the type of the data address.

In some embodiments, the identifier and content of the descriptor, andtensor data indicated by the descriptor can be stored in a same area.For example, a continuous area of an on-chip cache with addressesADDR0-ADDR1023 can be used to store the above information. Within thisarea, addresses ADDR0-ADDR31 can be used to store the identifier of thedescriptor, addresses ADDR32-ADDR63 can be used to store the content ofthe descriptor, and addresses ADDR64-ADDR1023 can be used to store thetensor data indicated by the descriptor. The address ADDR is not limitedto 1 bit or 1 byte, and is an address unit used to represent an address.Those skilled in the art can determine the storage area and the addressthereof according to the specific applications, which is not limited inthe present disclosure.

In some embodiments, the identifier and content of the descriptor, andthe tensor data indicated by the descriptor can be respectively storedin different areas of an internal memory. For example, a register can beused as a descriptor storage space to store the identifier and contentof the descriptor, and an on-chip cache can be used as a data storagespace to store the tensor data indicated by the descriptor.

In some embodiments, a special register (SR) may be provided for thedescriptor, where data in the descriptor may be an immediate number orbe obtained from the special register. When the register is used tostore the identifier and content of the descriptor, a serial number ofthe register can be used to indicate the identifier of the descriptor.For example, if the serial number of the register is 0, the identifierof a descriptor stored in the register is 0. When the descriptor in theregister is valid, an area can be allocated in a caching space (such ascreating a tensor caching unit for each piece of tensor data in thecache) according to a size of tensor data indicated by the descriptorfor storing the tensor data. It should be understood that a presetcaching space may also be used to store the tensor data, which is notlimited in the present disclosure.

In some embodiments, the identifier and content of the descriptor can bestored in an internal memory, and the tensor data indicated by thedescriptor can be stored in an external memory. For example, theidentifier and content of the descriptor may be stored on-chip and thetensor data indicated by the descriptor may be stored off-chip.

In some embodiments, the data address of the data storage spacecorresponding to the descriptor may be a fixed address. For example, aseparate data storage space may be allocated for the tensor data, and astart address of each piece of tensor data in the data storage spacecorresponds to an identifier of the descriptor. In this case, theprocessor can determine the data address of the tensor data according tothe content of the descriptor.

In some embodiments, when the data address of the data storage spacecorresponding to the identifier of the descriptor is a variable address,the descriptor may be also used to indicate the address of N-dimensionaltensor data, where the content of the descriptor may further include atleast one address parameter representing the address of the tensor data.For example, if the tensor data is a 3-dimensional data, when thedescriptor points to the address of the tensor data, the content of thedescriptor may include an address parameter indicating the address ofthe tensor data, such as a start address of the tensor data; or thecontent of the descriptor may include a plurality of address parametersof the address of the tensor data, such as a start address+addressoffset of the tensor data, or address parameters of the tensor data ineach dimension. Those skilled in the art can set the address parametersaccording to actual needs, which is not limited in the presentdisclosure.

In some embodiments, the address parameter of the tensor data includes abase address of the datum point of the descriptor in the data storagespace of the tensor data, where the base address may vary from differentdatum points. The present disclosure does not limit the selection of thedatum point.

In some embodiments, the base address may include a start address of thedata storage space. When the datum point of the descriptor is a firstdata block of the data storage space, the base address of the descriptoris the start address of the data storage space. When the datum point ofthe descriptor is other data than the first data block in the datastorage space, the base address of the descriptor is the physicaladdress of the data block in the data storage space.

In some embodiments, the shape parameter of the tensor data includes atleast one of the followings: a size of the data storage space of thetensor data in at least one of N dimensions, a size of the storage areain at least one of the N dimensions, an offset of the storage area in atleast one of the N dimensions, a position of at least two vertices atdiagonal positions in the N dimensions relative to the datum point, anda mapping relationship between a data description position of the tensordata indicated by the descriptor and the data address of the tensor dataindicated by the descriptor. The data description position is a mappingposition of a point or an area in the tensor data indicated by thedescriptor, for example, if the tensor data is 3-dimensional data, thedescriptor can use a coordinate (x, y, z) to represent the shape of thetensor data, and the data description position of the tensor data can berepresented by the coordinate (x, y, z), and the data descriptionposition of the tensor data may be a position of a point or an areawhere the tensor data is mapped in a 3-dimensional space.

It should be understood that those skilled in the art may select a shapeparameter representing tensor data according to actual conditions, whichis not limited in the present disclosure.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, when the space of the receiver of datasynchronization is insufficient, part of tensor data can be synchronizedfirstly, and repeated many times until all of the tensor data aresynchronized, which can avoid the problems of overall synchronizationfailure or synchronization delay of tensor data in the case ofinsufficient space, and improve the efficiency of data synchronization.In addition, the descriptor indicating the shape of the tensor data isset, and the tensor data is determined according to the descriptorduring the data synchronization process, thereby reducingsynchronization overhead and reducing the complexity of data access, andachieving the instruction compatibility during transfer and processingprocess.

It should be noted that, for the sake of simple description, the abovemethod embodiments are all described as a series of action combinations.However, those skilled in the art should be aware that the presentdisclosure is not limited by the described action order, becauseaccording to the present disclosure, certain steps may be executed inanother order or executed simultaneously. Those skilled in the artshould also be aware that the embodiments described in the specificationare alternative embodiments and that the actions and modules involvedare not necessary in the present disclosure.

It should be further noted that although the steps in the flow chartsare shown in sequence as indicated by the arrows, these steps are notnecessarily executed in the order indicated by the arrows. Unlessspecifically stated in the present disclosure, the execution of thesesteps is not strictly limited in order, and these steps may be executedin other orders. In addition, at least part of the steps in in the flowcharts may include a plurality of sub-steps or stages. These sub-stepsor stages are not necessarily executed at the same time, but may beexecuted at different times. The execution of these sub-steps or stagesis not necessarily performed sequentially, but may be performedalternately with other steps or at least a part of the sub-steps orstages of other steps.

FIG. 3d 2 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure. The datasynchronization apparatus is applied to a first processor. As shown inFIG. 3d 2, the data synchronization apparatus includes:

a characteristic determining module 51 d configured to, according to adescriptor of tensor data to be synchronized, determine datacharacteristics of the tensor data, where the descriptor is used toindicate a shape of the tensor data to be synchronized; and

a query instruction generating and sending module 52 d configured to,according to the data characteristics of the tensor data, generate astate query instruction and send the state query instruction to a secondprocessor, where the state query instruction is used to instruct thesecond processor to determine the amount of tensor data to besynchronized and generate a synchronization state instruction.

In some embodiments, the data synchronization apparatus furtherincludes:

a state instruction parsing module configured to, when thesynchronization state instruction is received from the second processor,parse the synchronization state instruction to determine the datacharacteristics of the tensor data to be synchronized and the amount ofdata that can be synchronized:

a first descriptor determining module configured to determine thedescriptor of the tensor data to be synchronized according to the datacharacteristics;

a data determining module configured to determine first sub-data of thetensor data according to the descriptor and the amount of data that canbe synchronized, where the amount of the first sub-data corresponds tothe amount of data that can be synchronized; and

a synchronization instruction generating and sending module configuredto, according to the first sub-data, generate a synchronizationinstruction and send the synchronization instruction to the secondprocessor to instruct the second processor to obtain the first sub-data.

In some embodiments, the data determining module includes:

a first determining sub-module configured to, according to thedescriptor, determine the tensor data to be synchronized and the secondsub-data in a to-be-synchronized state among the tensor data; and

a second determining sub-module configured to determine the firstsub-data according to the second sub-data and the amount of data thatcan be synchronized.

In some embodiments, the data synchronization apparatus includes:

a state changing module configured to change a state of the firstsub-data of the tensor data from a to-be synchronized state to asynchronized state.

FIG. 3d 3 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure. The datasynchronization apparatus is applied to a second processor. As shown inFIG. 3d 3, the data synchronization apparatus includes:

a query instruction parsing module 61 d configured to, when a statequery instruction is received from a first processor, parse the statequery instruction to obtain data characteristics of tensor data to besynchronized:

a second descriptor determining module 62 d configured to determine adescriptor of the tensor data to be synchronized according to the datacharacteristics, where the descriptor is used to indicate a shape of thetensor data to be synchronized;

a determining module 63 d of the amount of data configured to determinethe amount of data that can be synchronized among the tensor dataaccording to the descriptor of the tensor data; and

a state instruction generating and sending module 64 d configured to,according to the data characteristics of the tensor data and the amountof data that can be synchronized, generate a synchronization stateinstruction and sending the synchronization state instruction to thefirst processor, where the synchronization state instruction is used toinstruct the first processor to determine first sub-data of the tensordata, and the amount of the first sub-data corresponds to the amount ofdata that can be synchronized.

In some embodiments, the data synchronization apparatus furtherincludes:

a synchronization instruction parsing module configured to, when asynchronization instruction is received from the first processor, parsethe synchronization instruction to obtain the data characteristics ofthe tensor data to be synchronized and the first sub-data of the tensordata;

a third descriptor determining module configured to determine thedescriptor of the tensor data according to the data characteristics; and

a data storing module configured to store the first sub-data of thetensor data according to the descriptor of the tensor data.

It should be understood that the apparatus embodiment described above isonly schematic, and the apparatus provided in the present disclosure maybe implemented in other manners. For example, division of theunits/modules is only logical function division and another divisionmanner may be adopted during practical implementation. For example, aplurality of units or components may be combined or integrated intoanother system or some characteristics may be neglected or notperformed.

In addition, unless otherwise specified, each functional unit/module inthe embodiments of the disclosure may be integrated into a unit/module,each unit/module may also physically exist independently, and two ormore units/modules may also be integrated into one unit/module. Theintegrated unit/module may be implemented in the form of hardware or asoftware functional unit/module.

If the integrated unit/module is implemented in the form of hardware,the hardware may be a digital circuit, an analogue circuit, and thelike. The physical implementation of hardware may include, but is notlimited to, a transistor, a memristor, and the like. Unless otherwisespecified, the artificial intelligence processor may be any appropriatehardware processor, such as CPU, GPU, FPGA, DSP, ASIC, and the like.Unless otherwise specified, the storage unit may be any proper magneticstorage medium or magneto-optic storage medium, for example, an RRAM(Resistive Random Access Memory), a DRAM (Dynamic Random Access Memory),an SRAM (Static Random-Access Memory), an EDRAM (Enhanced Dynamic RandomAccess Memory), an HBM (High-Bandwidth Memory), an HMC (Hybrid MemoryCube), and the like.

If being implemented in the form of a software program module and soldor used as an independent product, the integrated unit/module may bestored in a computer-readable memory. Based on such an understanding,all or part of the technical solutions may be embodied in form ofsoftware product. The computer software product is stored in a memory,including a plurality of instructions configured to enable a computerdevice (which may be a PC, a server, a network device, or the like) toperform all or part of the operations of the method in each embodimentof the application. The memory may include various media capable ofstoring program codes such as a U disk, a mobile hard disk, a read-onlymemory (ROM), a random access memory (RAM), a magnetic disk, or anoptical disk.

In some embodiments, the present disclosure provides an artificialintelligence chip including the above-mentioned data synchronizationapparatus.

In some embodiments, the present disclosure provides a board cardincluding a storage device, an interface apparatus, a control device,and the above-mentioned artificial intelligence chip. The artificialintelligence chip is connected to the storage device, the controldevice, and the interface apparatus, respectively; the storage device isconfigured to store data; the interface apparatus is configured toimplement data transfer between the artificial intelligence chip and anexternal equipment; and the control device is configured to monitor astate of the artificial intelligence chip.

In the above-mentioned embodiments, the description of each embodimenthas its own focus. For parts that are not described in detail in anembodiment, please refer to related descriptions of other embodiments.The technical features of the above-mentioned embodiments may becombined arbitrarily. In order to make the description concise, not allpossible combinations of the various technical features in theabove-mentioned embodiments are described. However, as long as there isno contradiction in the combinations of these technical features, theyshould be regarded as the scope of this specification.

The foregoing may be better understood according to the followingarticles:

A1. A data synchronization method applied to a first processor,comprising:

according to a descriptor of tensor data to be synchronized, determiningdata characteristics of the tensor data, wherein the descriptor is usedto indicate a shape of the tensor data to be synchronized; and

according to the data characteristics of the tensor data, generating astate query instruction and sending the state query instruction to asecond processor, wherein the state query instruction is used toinstruct the second processor to determine the amount of tensor data tobe synchronized and generate a synchronization state instruction.

A2. The data synchronization method of A1, further comprising:

when the synchronization state instruction is received from the secondprocessor, parsing the synchronization state instruction to determinedata characteristics of the tensor data to be synchronized and theamount of data that can be synchronized;

determining the descriptor of the tensor data to be synchronizedaccording to the data characteristics;

determining first sub-data of the tensor data according to thedescriptor and the amount of data that can be synchronized, wherein theamount of the first sub-data corresponds to the amount of data that canbe synchronized; and

according to the first sub-data, generating a synchronizationinstruction and sending the synchronization instruction to the secondprocessor to instruct the second processor to obtain the first sub-data.

A3. The data synchronization method of A2, wherein the determining firstsub-data of the tensor data according to the descriptor and the amountof data that can be synchronized includes:

according to the descriptor, determining the tensor data to besynchronized and second sub-data in a to-be-synchronized state among thetensor data; and

determining the first sub-data according to the second sub-data and theamount of data that can be synchronized.

A4. The data synchronization method of A2 or A3, further comprising:

changing a state of the first sub-data of the tensor data from a to-besynchronized state to a synchronized state.

A5. A data synchronization method applied to a second processor,comprising:

when a state query instruction is received from a first processor,parsing the state query instruction to obtain data characteristics oftensor data to be synchronized;

determining a descriptor of the tensor data to be synchronized accordingto the data characteristics, wherein the descriptor is used to indicatea shape of the tensor data to be synchronized;

determining the amount of data that can be synchronized among the tensordata according to the descriptor of the tensor data; and

according to the data characteristics of the tensor data and the amountof data that can be synchronized, generating a synchronization stateinstruction and sending the synchronization state instruction to thefirst processor, wherein the synchronization state instruction is usedto instruct the first processor to determine first sub-data of thetensor data, and the amount of the first sub-data corresponds to theamount of data that can be synchronized.

A6. The data synchronization method of A5, further comprising:

when a synchronization instruction is received from the first processor,parsing the synchronization instruction to obtain the datacharacteristics of the tensor data to be synchronized and the firstsub-data of the tensor data;

determining the descriptor of the tensor data according to the datacharacteristic; and

storing the first sub-data of the tensor data according to thedescriptor of the tensor data.

A7. A data synchronization apparatus applied to a first processor,comprising:

a characteristic determining module configured to, according to adescriptor of tensor data to be synchronized, determine datacharacteristics of the tensor data, wherein the descriptor is used toindicate a shape of the tensor data to be synchronized; and

a query instruction generating and sending module configured to,according to the data characteristics of the tensor data, generate astate query instruction and send the state query instruction to a secondprocessor, wherein the state query instruction is used to instruct thesecond processor to determine the amount of tensor data to besynchronized and generate a synchronization state instruction.

A8. The data synchronization apparatus of A7, further comprising:

a state instruction parsing module configured to, when thesynchronization state instruction is received from the second processor,parse the synchronization state instruction to determine the datacharacteristics of the tensor data to be synchronized and the amount ofdata that can be synchronized:

a first descriptor determining module configured to determine thedescriptor of the tensor data to be synchronized according to the datacharacteristics;

a data determining module configured to determine first sub-data of thetensor data according to the descriptor and the amount of data that canbe synchronized, wherein the amount of the first sub-data corresponds tothe amount of data that can be synchronized; and

a synchronization instruction generating and sending module configuredto, according to the first sub-data, generate a synchronizationinstruction and send the synchronization instruction to the secondprocessor to instruct the second processor to obtain the first sub-data.

A9. The data synchronization apparatus of A8, wherein the datadetermining module includes:

a first determining sub-module configured to, according to thedescriptor, determine the tensor data to be synchronized and the secondsub-data in a to-be-synchronized state among the tensor data;

a second determining sub-module configured to determine the firstsub-data according to the second sub-data and the amount of data thatcan be synchronized.

A10. The data synchronization apparatus of A8 or A9, further comprising:

a state changing module configured to change a state of the firstsub-data of the tensor data from a to-be synchronized state to asynchronized state.

A11. A data synchronization apparatus applied to a second processor,comprising:

a query instruction parsing module configured to, when a state queryinstruction is received from a first processor, parse the state queryinstruction to obtain data characteristics of tensor data to besynchronized;

a second descriptor determining module configured to determine adescriptor of the tensor data to be synchronized according to the datacharacteristics, wherein the descriptor is used to indicate a shape ofthe tensor data to be synchronized;

a data amount determining module configured to determine the amount ofdata that can be synchronized among the tensor data according to thedescriptor of the tensor data; and

a state instruction generating and sending module configured to,according to the data characteristics of the tensor data and the amountof data that can be synchronized, generate a synchronization stateinstruction and send the synchronization state instruction to the firstprocessor, wherein the synchronization state instruction is used toinstruct the first processor to determine first sub-data of the tensordata, and the amount of the first sub-data corresponds to the amount ofdata that can be synchronized.

A12. The data synchronization apparatus of A11, further comprising:

a synchronization instruction parsing module configured to, when asynchronization instruction is received from the first processor, parsethe synchronization instruction to obtain the data characteristics ofthe tensor data to be synchronized and the first sub-data of the tensordata;

a third descriptor determining module configured to determine thedescriptor of the tensor data according to the data characteristics; and

a data storing module configured to store the first sub-data of thetensor data according to the descriptor of the tensor data.

A13. An artificial intelligence chip, comprising the datasynchronization apparatus of any one of A7-A12.

A14. An electronic device, comprising the artificial intelligence chipof A13.

A15. A board card, comprising a storage device, an interface apparatus,a control device, and the artificial intelligence chip of A13, wherein

the artificial intelligence chip is connected to the storage device, thecontrol device, and the interface apparatus, respectively;

the storage device is configured to store data;

the interface apparatus is configured to implement data transfer betweenthe artificial intelligence chip and an external equipment; and

the control device is configured to monitor a state of the artificialintelligence chip.

A16. The board card of A15, wherein

the storage device includes a plurality of groups of storage units,wherein each group of the storage units is connected with the artificialintelligence chip by a bus, and the storage units are DDR SDRAMs;

the chip includes a DDR controller configured to control data transferand data storage of each storage unit; and

the interface apparatus is a standard PCIE interface.

With the continuous development of the A1 (Artificial Intelligence)technology, it has gradually obtained wide application and worked wellin the fields of image recognition, speech recognition, and naturallanguage processing, and the like. However, as the complexity of A1algorithms is growing, the amount of data and data dimensions that needto be processed are increasing, therefore, multi-core and/or multi-chipdata are usually required for data processing. When data is synchronizedbetween cores or chips, a synchronization method adopting therelated-art may result in large synchronization overhead and lowprocessing efficiency.

In some embodiments, the present disclosure provides a datasynchronization method. FIG. 1g shows a flowchart of a datasynchronization method according to an embodiment of the presentdisclosure. As shown in FIG. 1g , the data synchronization method isapplied to a first processor (any processor in a processing system), andthe method includes:

a step S11 g: when a descriptor synchronization request instruction isreceived from a second processor, determining a descriptor of tensordata to be synchronized and the amount of data that can be synchronizedamong the tensor data, where the descriptor is used to indicate a shapeof the tensor data to be synchronized;

a step S12 g: determining first sub-data of the tensor data according tothe descriptor of the tensor data and the amount of data that can besynchronized, where the amount of the first sub-data corresponds to theamount of data that can be synchronized; and

a step S13 g: according to the first sub-data, generating a descriptorsynchronization instruction and sending the descriptor synchronizationinstruction to the second processor to instruct the second processor toobtain the first sub-data.

For example, the data to be synchronized may include N-dimensionaltensor data (N is an integer greater than or equal to 0, for example,N=1, 2, or 3).

In some embodiments, during data processing, data synchronizationbetween a plurality of processors (such as a plurality of cores of anartificial intelligence chip) may be executed, for example, an operationresult of a processor A1 may be synchronized to a processor A2 as inputdata of another operation. In this case, a data synchronizationmechanism based on the descriptor can be used to achieve datasynchronization.

In some embodiments, since a non-shared storage space of each processorallocated to the tensor data to be synchronized may be limited, thetensor data cannot be synchronized at the same time. In this case, partof tensor data can be synchronized firstly, and repeated many timesuntil all of the tensor data are synchronized.

In some embodiments, the receiver of data synchronization can issue asynchronization request for part of tensor data, in other words, thereceiver sends a descriptor synchronization request instruction, wherethe descriptor synchronization request instruction may determine thedescriptor of the tensor data to be synchronized and the amount of datathat can be synchronized among the tensor data, i.e., the amount of datathat can be contained in the non-shared storage space of the receiverallocated to the tensor data.

In some embodiments, the first processor among a plurality of processorsmay be set as the sender of data to be synchronized, and the secondprocessor may be set as the receiver of data synchronization. Both thefirst processor and the second processor are any of the plurality ofprocessors, and the second processor may be of the same type ordifferent from the first processor. The present disclosure does notlimit the type of the first processor and the type of the secondprocessor.

In some embodiments, in the step S11 g, when receiving the descriptorsynchronization request instruction from the second processor, the firstprocessor may parse the instruction to obtain content of the instruction(for example, an identifier of the descriptor of the tensor data to besynchronized, data characteristics of the tensor data to besynchronized, the amount of data that can be synchronized, and thelike), thereby determining the descriptor of the tensor data to besynchronized and the amount of data that can be synchronized.

In some embodiments, in the step S12 g, the first processor maydetermine the tensor data to be synchronized according to thedescriptor, and determine the part of data that can be synchronized thistime from the tensor data according to the amount of data that can besynchronized, i.e., the first sub-data. The amount of the first sub-datamay correspond to the amount of data that can be synchronized, forexample, the amount of the first sub-data may less than or equal to theamount of data that can be synchronized.

In some embodiments, if all the data of the tensor data has not beensynchronized, data that can be synchronized may be selected from thetensor data as the first sub-data. If part of the tensor data has notbeen synchronized, and the amount of data that has not been synchronizedis greater than the amount of data that can be synchronized, data thatcan be synchronized may be selected from the data that has not beensynchronized (second sub-data of the tensor data) as the first sub-data;if the amount of data that has not been synchronized is less than orequal to the amount of data that can be synchronized, the data that hasnot been synchronized can be directly taken as the first sub-data. Itshould be understood that those skilled in the art can determine thefirst sub-data according to the actual situation, which is not limitedin the present disclosure.

In some embodiments, in the step S13 g, the first processor may generatea descriptor synchronization instruction according to the first sub-dataand send the descriptor synchronization instruction to the secondprocessor, where the descriptor synchronization instruction may includethe identifier of the descriptor of the tensor data to be synchronizedand the first sub-data of the tensor data. After receiving thedescriptor synchronization instruction, the second processor may parsethe instruction to determine the descriptor of the tensor data to besynchronized and the first sub-data of the tensor data, determine thetensor data to be synchronized according to the descriptor, and storethe first sub-data of the tensor data in its own non-shared storagespace.

According to the above-mentioned data synchronization method provided bythe present disclosure, by setting the descriptor indicating the shapeof the tensor data, the tensor data can be determined according to thedescriptor in the descriptor synchronization request instruction. Thesub-data synchronized this time can be determined according to theamount of data that can be synchronized of the receiver, and thedescriptor synchronization instruction can be generated and sentaccording to the sub-data, so that the receiver can obtain the sub-datasynchronized this time. In this way, the synchronization overhead can bereduced and the efficiency of data synchronization can be improved.

In some embodiments, the descriptor synchronization request instructionmay include an identifier of the descriptor. The step S11 g includes:

parsing the descriptor synchronization request instruction to obtain theidentifier of the descriptor and the amount of data that can besynchronized; and

determining the descriptor of the tensor data to be synchronizedaccording to the identifier of the descriptor.

For example, if a descriptor indicating the tensor data to besynchronized are registered in the first processor and the secondprocessor, the descriptor synchronization instruction may only includethe identifier of the descriptor (for example, the descriptorsynchronization instruction may be represented as Send TR1 when theidentifier of the descriptor is TR1) and the amount of data that can besynchronized. The first processor may parse the descriptorsynchronization request instruction to obtain the identifier of thedescriptor and the amount of data that can be synchronized, and thendetermine the descriptor of the tensor data to be synchronized accordingto the identifier of the descriptor. In this way, the amount of datatransmitted during synchronization can be reduced, and the processingefficiency can be improved.

In some embodiments, the descriptor synchronization request instructionincludes the data characteristics of the tensor data to be synchronized.The step S11 g includes:

parsing the descriptor synchronization request instruction to obtain thedata characteristics of the tensor data to be synchronized and theamount of data that can be synchronized; and

determining the descriptor of the tensor data according to the datacharacteristics of the tensor data.

For example, if the identifier of the descriptor already registered inthe first processor does not correspond to the identifier of thedescriptor of the tensor data determined in the descriptorsynchronization request instruction, the descriptor synchronizationinstruction may include the data characteristics of the tensor data tobe synchronized, where the data characteristics of the tensor data to besynchronized may include information such as the shape, source, andaddress of the tensor data. For example, the tensor data may be from aK-th sender (a K-th processor), the tensor data may be from an operationresult of a convolution operation numbered 200, the address of thetensor data may be a specific address area (for example, an addressADDR0-ADDR127), and the shape of the tensor data may be a specifiedshape (for example, the tensor data may be a 20*10 two-dimensionaltensor). Those skilled in the art can set the data characteristics ofthe tensor data to be synchronized according to the actual situation,which is not limited in the present disclosure.

In some embodiments, according to the data characteristics, the firstprocessor may determine the tensor data to be synchronized, anddetermine the descriptor of the tensor data to be synchronized, forexample, the first processor may directly obtain a descriptor orregister a corresponding descriptor. According to the descriptor of thetensor data to be synchronized, the tensor data may be determined, andthen the sub-data to be synchronized this time may be determinedaccording to the amount of data that can be synchronized.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the descriptor of the tensor data to besynchronized can be determined according to the data characteristics inthe descriptor synchronization request instruction, so as to achieve thesynchronization of part of the tensor data. In this way, there is noneed to transfer tensor data itself during synchronization, whichreduces the amount of transferred data and synchronization overhead, andimproves processing the efficiency.

In some embodiments, the step S12 g includes:

determining the tensor data and second sub-data in a to-be-synchronizedstate among the tensor data according to the descriptor of the tensordata; and

determining the first sub-data according to the second sub-data and theamount of data that can be synchronized.

For example, the state of data among the tensor data may be set, wherethe data that has been synchronized may be set to a synchronized state,and the data that has not been synchronized may be set to ato-be-synchronized state. In this case, when receiving the descriptorsynchronization request instruction from the second processor, the firstprocessor may determine the tensor data to be synchronized according tothe descriptor; according to the state of the data among the tensordata, the first processor may determine the second sub-data in theto-be-synchronized state; and according to the second sub-data and theamount of data that can be synchronized indicated by the descriptorsynchronization request instruction, the first processor may determinethe first sub-data to be synchronized this time.

In some embodiments, if the amount of the second sub-data is greaterthan the amount of data that can be synchronized, the first sub-datasynchronized this time can be selected from the second sub-data; if theamount of the second sub-data is less than or equal to the amount ofdata that can be synchronized, the second sub-data can be directly takenas the first sub-data.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, part of tensor data synchronized this time canbe determined, and then the part of tensor data can be synchronized,thereby improving the efficiency of data synchronization.

In some embodiments, the data synchronization method further includes:changing the state of the first sub-data of the tensor data from theto-be synchronized state to the synchronized state.

For example, after the first processor generates the descriptorsynchronization instruction according to the first sub-data of thetensor data and sends the descriptor synchronization instruction to thesecond processor to make the second processor synchronize the firstsub-data of the tensor data, the first processor can change the state ofdata among the tensor data. In other words, the first processor canchange the state of the first sub-data from the to-be synchronized stateto the synchronized state. In this way, when the synchronization staterequest of the second processor is received, data to be synchronizednext time can be determined from part of data in the to-be-synchronizedstate, thereby avoiding repeated data synchronization and improving datasynchronization efficiency.

FIG. 3e 1 shows a flowchart of a data synchronization method accordingto an embodiment of the present disclosure. As shown in FIG. 3e 1, thedata synchronization method is applied to a second processor, and thedata synchronization method includes:

a step S31 e: generating a descriptor synchronization requestinstruction according to a descriptor of tensor data to be synchronizedand the amount of data that can be synchronized among the tensor data,where the descriptor is used to indicate a shape of the tensor data tobe synchronized, the descriptor synchronization request instruction isused to instruct a first processor to determine the descriptor of thetensor data to be synchronized and first sub-data of the tensor dataaccording to the descriptor synchronization request instruction, and theamount of the first sub-data corresponds to the amount of data that canbe synchronized; and

a step S32 e: sending the descriptor synchronization request instructionto the first processor.

For example, the second processor among a plurality of processors may beset to be a receiver of data synchronization, and the second processormay issue the synchronization request for part of the tensor data. Inthe step S31 e, when there is tensor data to be synchronized in thesecond processor, the second processor can determine the descriptor ofthe tensor data and the amount of data that can be contained in thenon-shared storage space of the second processor allocated to the tensordata, i.e., the amount of data can be synchronized. According to thedescriptor of the tensor data and the amount of data that can besynchronized, the second processor may generate a descriptorsynchronization request instruction and send the instruction in the stepS32 e The descriptor synchronization request instruction may include atleast one of an identifier of the descriptor, content of the descriptor,and data characteristic of the tensor data, and the descriptorsynchronization request instruction is used to instruct the firstprocessor to determine the descriptor of the tensor data to besynchronized and the first sub-data of the tensor data.

In some embodiments, when receiving the descriptor synchronizationrequest instruction, the first processor may parse the instruction todetermine the descriptor of the tensor data to be synchronized and theamount of data that can be synchronized; the first processor maydetermine the tensor data to be synchronized according to thedescriptor; and the first processor may determine the part of data thatcan be synchronized this time from the tensor data according to theamount of data that can be synchronized, i.e., the first sub-data. Theamount of the first sub-data may correspond to the amount of data thatcan be synchronized, for example, the amount of the first sub-data mayless than or equal to the amount of data that can be synchronized.

In some embodiments, if all the data of the tensor data has not beensynchronized, data that can be synchronized may be selected from thetensor data as the first sub-data. If part of the tensor data has notbeen synchronized, and the amount of data that has not been synchronizedis greater than the amount of data that can be synchronized, data thatcan be synchronized may be selected from the data that has not beensynchronized (second sub-data of the tensor data) as the first sub-data;if the amount of data that has not been synchronized is less than orequal to the amount of data that can be synchronized, the data that hasnot been synchronized can be directly taken as the first sub-data. Itshould be understood that those skilled in the art can determine thefirst sub-data according to the actual situation, which is not limitedin the present disclosure.

In some embodiments, the descriptor synchronization request instructionmay also include a range of part of tensor data to be synchronized, suchas the content of the descriptor of the part of sub-data or a range of astorage address of the part of sub-data, so as to determine the part ofdata to be synchronized.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the receiver can issue a synchronization requestfor part of the tensor data, so that the sender can determine thesub-data to be synchronized this time, thereby improving the efficiencyof data synchronization.

In some embodiments, the data synchronization method further includes:

when a descriptor synchronization instruction is received from a firstprocessor, determining a descriptor of tensor data to be synchronizedand first sub-data of the tensor data; and

storing the first sub-data of the tensor data according to thedescriptor of the tensor data.

For example, the first processor may generate and send a descriptorsynchronization instruction according to the descriptor of the tensordata and the first sub-data. When receiving the descriptorsynchronization instruction, the second processor may parse theinstruction to determine the descriptor of the tensor data to besynchronized and the first sub-data of the tensor data synchronized thistime; and then the second processor may determine the tensor data to besynchronized according to the descriptor, and store the first sub-dataof the tensor data in its own non-shared storage space.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the receiver can determine the descriptoraccording to the descriptor synchronization instruction and obtainsub-data synchronized this time, thereby reducing synchronizationoverhead and improving the efficiency of data synchronization.

In some embodiments, when there is tensor data to be synchronized in thesender of data to be synchronized, for example, when the first processorcompletes an operation and obtains an operation result (the tensordata), the sender can query the state of the receiver to determine theamount of data that can be contained in the non-shared storage space ofthe receiver allocated to the tensor data, so that part of tensor datacan be synchronized.

In some embodiments, the present disclosure provides a datasynchronization method applied to a first processor, including:

according to a descriptor of tensor data to be synchronized, generatinga state query instruction, where the descriptor is used to indicate ashape of the tensor data to be synchronized, and the state queryinstruction is used to instruct a second processor to determine theamount of tensor data to be synchronized and generate a synchronizationstate instruction, where the state query instruction includes anidentifier of the descriptor and/or content of the descriptor; and

sending the state query instruction to the second processor.

For example, when there is tensor data to be synchronized in the senderof data to be synchronized, when the first processor completes anoperation and obtains an operation result (the tensor data), the sendercan query the state of the receiver to determine the amount of data thatcan be contained in the non-shared storage space of the receiverallocated to the tensor data, so that part of tensor data can besynchronized. The first processor among a plurality of processors may beset as the sender of data to be synchronized, and the second processormay be set as the receiver of data synchronization. Both the firstprocessor and the second processor are any of the plurality ofprocessors, and the second processor may be of the same type ordifferent from the first processor. The present disclosure does notlimit the type of the first processor and the type of the secondprocessor.

In some embodiments, the first processor may generate a state queryinstruction according to the descriptor of the tensor data to besynchronized. The state query instruction may include an identifier ofthe descriptor of the tensor data to be synchronized and/or content ofthe descriptor, and the state query instruction is used to instruct thesecond processor to determine and reply its own state (the amount ofdata that can be synchronized among the tensor data).

In some embodiments, the first processor may send the state queryinstruction to the second processor. After receiving the state queryinstruction, the second processor may parse the instruction to determinethe identifier of the descriptor and/or the content of the descriptor.According to the identifier of the descriptor and/or the content of thedescriptor, the second processor may determine the tensor data to besynchronized, and then determine the space that can be allocated to thetensor data, and determine the amount of data that can be synchronizedamong the tensor data. According to the amount of data that can besynchronized among the tensor data and descriptor, the second processorcan generate and send a synchronization state instruction, so that thefirst processor can determine the descriptor of the tensor data to besynchronized and the amount of data that can be synchronized of thistime.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the sender of data to be synchronized mayactively query the state of the receiver, so that part of data can besynchronized between the sender and the receiver, thereby improving theefficiency of data synchronization.

In some embodiments, the data synchronization method further includes:

when the synchronization state instruction is received from the secondprocessor, determining first sub-data of the tensor data according tothe descriptor of the tensor data in the synchronization stateinstruction and the amount of data that can be synchronized, where theamount of the first sub-data corresponds to the amount of data that canbe synchronized; and

according to the first sub-data, generating a descriptor synchronizationinstruction and sending the descriptor synchronization instruction tothe second processor to instruct the second processor to obtain thefirst sub-data.

For example, when receiving the synchronization state instruction fromthe second processor, the first processor may parse the instruction toobtain the content of the instruction (for example, the identifier ofthe descriptor, the amount of data that can be synchronized, etc.).According to the identifier of the descriptor, the descriptor of thetensor data to be synchronized can be determined, so as to determine thetensor data to be synchronized; and the part of data that can besynchronized this time (the first sub-data) is determined from thetensor data according to the amount of data that can be synchronized.The amount of the first sub-data may correspond to the amount of datathat can be synchronized, for example, the amount of the first sub-datamay be less than or equal to the amount of data that can besynchronized.

In some embodiments, if all the data of the tensor data has not beensynchronized, data that can be synchronized may be selected from thetensor data as the first sub-data. If part of the tensor data has notbeen synchronized, and the amount of data that has not been synchronizedis greater than the amount of data that can be synchronized, data thatcan be synchronized may be selected from the data that has not beensynchronized (second sub-data of the tensor data) as the first sub-data;if the amount of data that has not been synchronized is less than orequal to the amount of data that can be synchronized, the data that hasnot been synchronized can be directly taken as the first sub-data. Itshould be understood that those skilled in the art can determine thefirst sub-data according to the actual situation, which is not limitedin the present disclosure.

In some embodiments, the synchronization state instruction may alsoinclude a range of part of tensor data to be synchronized, such as thecontent of the descriptor of the part of sub-data or a range of astorage address of the part of sub-data, so as to determine the part ofdata to be synchronized. The first processor may directly determine thefirst sub-data to be synchronized according to the range of the part ofdata.

In some embodiments, the first processor may generate a descriptorsynchronization instruction according to the first sub-data and send thedescriptor synchronization instruction to the second processor. Thedescriptor synchronization instruction may include the identifier of thedescriptor of the tensor data to be synchronized and the first sub-data.After receiving the descriptor synchronization instruction, the secondprocessor may parse the descriptor synchronization instruction todetermine the descriptor of the tensor data to be synchronized and thefirst sub-data of the tensor data, determine the tensor data to besynchronized according to the descriptor, and store the first sub-dataof the tensor data in its own non-shared storage space.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the tensor data can be determined according tothe descriptor in the synchronization state instruction, the sub-datasynchronized this time can be determined according to the amount of datathat can be synchronized of the receiver, and the descriptorsynchronization instruction can be generated and sent according to thesub-data, so that the receiver can obtain the sub-data synchronized thistime, thereby reducing the synchronization overhead and improving theefficiency of data synchronization.

In some embodiments, the synchronization state instruction includes theidentifier of the descriptor. The step of determining first sub-data ofthe tensor data according to the descriptor of the tensor data in thesynchronization state instruction and the amount of data that can besynchronized when the synchronization state instruction is received fromthe second processor includes:

parsing the synchronization state instruction to obtain the identifierof the descriptor and the amount of data that can be synchronized; and

according to the identifier of the descriptor, determining thedescriptor of the tensor data to be synchronized.

For example, the synchronization state instruction may include theidentifier of the descriptor (for example, the identifier is TR1) andthe amount of data that can be synchronized. The first processor mayparse the synchronization state instruction to obtain the identifier ofthe descriptor and the amount of data that can be synchronized, and thendetermine the descriptor of the tensor data to be synchronized accordingto the identifier of the descriptor.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the amount of data transmitted duringsynchronization can be reduced, and the processing efficiency can beimproved.

In some embodiments, the step of determining first sub-data of thetensor data according to the descriptor of the tensor data in thesynchronization state instruction and the amount of data that can besynchronized when the synchronization state instruction is received fromthe second processor includes:

determining the tensor data and second sub-data to be synchronized amongthe tensor data according to the descriptor of the tensor data; and

determining the first sub-data according to the second sub-data and theamount of data that can be synchronized in the synchronization stateinstruction.

For example, the state of data among the tensor data may be set, wherethe data that has been synchronized may be set to a synchronized state,and the data that has not been synchronized may be set to ato-be-synchronized state. In this case, when receiving thesynchronization state instruction from the second processor, the firstprocessor may determine the tensor data to be synchronized according tothe descriptor; according to the state of the data among the tensordata, the first processor may determine the second sub-data in theto-be-synchronized state; and according to the second sub-data and theamount of data that can be synchronized indicated by the synchronizationstate instruction, the first processor may determine the first sub-datato be synchronized this time.

In some embodiments, if the amount of the second sub-data is greaterthan the amount of data that can be synchronized, the first sub-datasynchronized this time can be selected from the second sub-data; if theamount of the second sub-data is less than or equal to the amount ofdata that can be synchronized, the second sub-data can be directly takenas the first sub-data.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, part of tensor data synchronized this time canbe determined, and then the part of tensor data can be synchronized,thereby improving the efficiency of data synchronization.

In some embodiments, the data synchronization method further includes:changing a state of the first sub-data of the tensor data from a to-besynchronized state to a synchronized state.

For example, after the first processor generates the descriptorsynchronization instruction according to the first sub-data of thetensor data and sends the descriptor synchronization instruction to thesecond processor to make the second processor synchronize the firstsub-data of the tensor data, the first processor can change the state ofdata among the tensor data, in other words, the first processor canchange the state of the first sub-data from the to-be synchronized stateto the synchronized state. In this way, when the state of the secondprocessor is queried next time and the synchronization state instructionof the second processor is received, data to be synchronized next timecan be determined from part of data in the to-be-synchronized state,thereby avoiding repeated data synchronization and improving datasynchronization efficiency.

In some embodiments, the present disclosure provides a datasynchronization method applied to a first processor, including:

when a state query instruction is received from a first processor,determining a descriptor of tensor data to be synchronized, where thedescriptor is used to indicate a shape of the tensor data to besynchronized;

determining the amount of data that can be synchronized among the tensordata according to the descriptor of the tensor data;

according to the descriptor of the tensor data and the amount of datathat can be synchronized, generating a synchronization stateinstruction, where the synchronization state instruction is used toinstruct the first processor to determine first sub-data of the tensordata, and the amount of the first sub-data corresponds to the amount ofdata that can be synchronized; and

sending the synchronization state instruction to the first processor.

For example, when there is tensor data to be synchronized in a sender ofdata to be synchronized, the sender may query the state of the receiver.The first processor (sender) may generate and send the state queryinstruction, and when the second processor receives the state queryinstruction, the second processor may parse the state query instructionto determine the descriptor of the tensor data to be synchronized.

In some embodiments, the second processor may determine the tensor datato be synchronized according to the descriptor, and determine the amountof data that can be contained in the non-shared storage space of thesecond processor allocated to the tensor data, i.e., the amount of datacan be synchronized, so that part of tensor data can be synchronized.

In some embodiments, the second processor may generate and send asynchronization state instruction to the first processor according tothe determined amount of data that can be synchronized and thedescriptor of the tensor data to instruct the first processor todetermine the descriptor of the tensor data to be synchronized and theamount of data that can be synchronized this time. After determining thepart of the data that can be synchronized this time (i.e., the firstsub-data), the first processor may generate the descriptorsynchronization instruction and send the descriptor synchronizationinstruction to the second processor, where the descriptorsynchronization instruction may include the identifier of the descriptorof the tensor data to be synchronized and the first sub-data.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the sender may query the state of the receiver;after receiving the state query instruction, the receiver determines andresponds to its own state (i.e., the amount of data that can besynchronized). In this way, part of tensor data can be synchronizedthrough interaction, which may improve the efficiency of datasynchronization.

In some embodiments, the data synchronization method further includes:

when the descriptor synchronization instruction is received from thefirst processor, determining the descriptor of the tensor data to besynchronized and first sub-data of the tensor data; and

according to the descriptor of the tensor data, storing the firstsub-data of the tensor data.

For example, when receiving the descriptor synchronization instruction,the second processor may parse the instruction to determine thedescriptor of the tensor data to be synchronized and the first sub-dataof the tensor data to be synchronized this time; and then the secondprocessor may determine the tensor data to be synchronized according tothe descriptor, and store the first sub-data of the tensor data in itsown non-shared storage space.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the receiver can determine the descriptoraccording to the descriptor synchronization instruction and obtainsub-data synchronized this time, thereby reducing synchronizationoverhead and improving the efficiency of data synchronization.

In some embodiments, the identifier and content of the descriptor can bestored in the descriptor storage space, where the descriptor storagespace can be a storage space in an internal memory (such as a register,an on-chip SRAM, or other medium cache, etc.) of the processor. The datastorage space of the tensor data indicated by the descriptor may be astorage space in the internal memory (such as an on-chip cache) of theprocessor or a storage space in an external memory (an off-chip memory)connected to the processor. The data address in the data storage spacemay be an actual physical address or a virtual address. The presentdisclosure does not limit a position of the descriptor storage space anda position of the data storage space, and the type of the data address.

In some embodiments, the identifier and content of the descriptor, andtensor data indicated by the descriptor can be stored in a same area.For example, a continuous area of an on-chip cache with addressesADDR0-ADDR1023 can be used to store the above information. Within thisarea, addresses ADDR0-ADDR31 can be used to store the identifier of thedescriptor, addresses ADDR32-ADDR63 can be used to store the content ofthe descriptor, and addresses ADDR64-ADDR1023 can be used to store thetensor data indicated by the descriptor. The address ADDR is not limitedto 1 bit or 1 byte, and is an address unit used to represent an address.Those skilled in the art can determine the storage area and the addressthereof according to the specific applications, which is not limited inthe present disclosure.

In some embodiments, the identifier and content of the descriptor, andthe tensor data indicated by the descriptor can be respectively storedin different areas of an internal memory. For example, a register can beused as a descriptor storage space to store the identifier and contentof the descriptor, and an on-chip cache can be used as a data storagespace to store the tensor data indicated by the descriptor.

In some embodiments, a special register (SR) may be provided for thedescriptor, where data in the descriptor may be an immediate number orbe obtained from the special register. When the register is used tostore the identifier and content of the descriptor, a serial number ofthe register can be used to indicate the identifier of the descriptor.For example, if the serial number of the register is 0, the identifierof a descriptor stored in the register is 0. When the descriptor in theregister is valid, an area can be allocated in a caching space (such ascreating a tensor caching unit for each piece of tensor data in thecache) according to a size of tensor data indicated by the descriptorfor storing the tensor data. It should be understood that a presetcaching space may also be used to store the tensor data, which is notlimited in the present disclosure.

In some embodiments, the identifier and content of the descriptor can bestored in an internal memory, and the tensor data indicated by thedescriptor can be stored in an external memory. For example, theidentifier and content of the descriptor may be stored on-chip and thetensor data indicated by the descriptor may be stored off-chip.

In some embodiments, the data address of the data storage spacecorresponding to the descriptor may be a fixed address. For example, aseparate data storage space may be allocated for the tensor data, and astart address of each piece of tensor data in the data storage spacecorresponds to an identifier of the descriptor. In this case, theprocessor can determine the data address of the tensor data according tothe content of the descriptor.

In some embodiments, when the data address of the data storage spacecorresponding to the identifier of the descriptor is a variable address,the descriptor may be also used to indicate the address of N-dimensionaltensor data, where the content of the descriptor may further include atleast one address parameter representing the address of the tensor data.For example, if the tensor data is 3-dimensional data, when thedescriptor points to the address of the tensor data, the content of thedescriptor may include an address parameter indicating the address ofthe tensor data, such as a start address of the tensor data; or thecontent of the descriptor may include a plurality of address parametersof the address of the tensor data, such as a start address+addressoffset of the tensor data, or address parameters of the tensor data ineach dimension. Those skilled in the art can set the address parametersaccording to actual needs, which is not limited in the presentdisclosure.

In some embodiments, the address parameter of the tensor data includes abase address of the datum point of the descriptor in the data storagespace of the tensor data, where the base address may vary from differentdatum points. The present disclosure does not limit the selection of thedatum point.

In some embodiments, the base address may include a start address of thedata storage space. When the datum point of the descriptor is a firstdata block of the data storage space, the base address of the descriptoris the start address of the data storage space. When the datum point ofthe descriptor is other data than the first data block in the datastorage space, the base address of the descriptor is the physicaladdress of the data block in the data storage space.

In some embodiments, the shape parameter of the tensor data includes atleast one of the followings: a size of the data storage space of thetensor data in at least one of N dimensions, a size of the storage areain at least one of the N dimensions, an offset of the storage area in atleast one of the N dimensions, a position of at least two vertices atdiagonal positions in the N dimensions relative to the datum point, anda mapping relationship between a data description position of the tensordata indicated by the descriptor and the data address of the tensor dataindicated by the descriptor. The data description position is a mappingposition of a point or an area in the tensor data indicated by thedescriptor, for example, if the tensor data is 3-dimensional data, thedescriptor can use a coordinate (x, y, z) to represent the shape of thetensor data, and the data description position of the tensor data can berepresented by the coordinate (x, y, z), and the data descriptionposition of the tensor data may be a position of a point or an areawhere the tensor data is mapped in a 3-dimensional space.

It should be understood that those skilled in the art may select a shapeparameter representing tensor data according to actual conditions, whichis not limited in the present disclosure.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, when the space of the receiver of datasynchronization is insufficient, part of tensor data can be synchronizedfirstly, and repeated many times until all of the tensor data aresynchronized, which can avoid the problems of overall synchronizationfailure or synchronization delay of tensor data in the case ofinsufficient space, and improve the efficiency of data synchronization.In addition, the descriptor indicating the shape of the tensor data isset, and the tensor data is determined according to the descriptorduring the data synchronization process, thereby reducingsynchronization overhead and reducing the complexity of data access.

It should be noted that, for the sake of simple description, the abovemethod embodiments are all described as a series of action combinations.However, those skilled in the art should be aware that the presentdisclosure is not limited by the described action order, becauseaccording to the present disclosure, certain steps may be executed inanother order or executed simultaneously. Those skilled in the artshould also be aware that the embodiments described in the specificationare alternative embodiments and that the actions and modules involvedare not necessary in the present disclosure.

It should be further noted that although the steps in the flow chartsare shown in sequence as indicated by the arrows, these steps are notnecessarily executed in the order indicated by the arrows. Unlessspecifically stated in the present disclosure, the execution of thesesteps is not strictly limited in order, and these steps may be executedin other orders. In addition, at least part of the steps in in the flowcharts may include a plurality of sub-steps or stages. These sub-stepsor stages are not necessarily executed at the same time, but may beexecuted at different times. The execution of these sub-steps or stagesis not necessarily performed sequentially, but may be performedalternately with other steps or at least a part of the sub-steps orstages of other steps.

FIG. 3e 2 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure. The datasynchronization apparatus is applied to a first processor. As shown inFIG. 3e 2, the data synchronization apparatus includes:

a descriptor and data amount determining module 5 e configured to, whena descriptor synchronization request instruction is received from asecond processor, determine a descriptor of tensor data to besynchronized and an amount of data that can be synchronized among thetensor data, where the descriptor is used to indicate a shape of thetensor data to be synchronized;

a sub-data determining module 52 e configured to, determine firstsub-data of the tensor data according to the descriptor of the tensordata and the amount of data that can be synchronized, where the amountof the first sub-data corresponds to the amount of data that can besynchronized; and

a synchronization instruction generating and sending module 53 econfigured to, according to the first sub-data, generate a descriptorsynchronization instruction and send the descriptor synchronizationinstruction to the second processor to instruct the second processor toobtain the first sub-data.

In some embodiments, the sub-data determining module includes:

a first determining sub-module configured to determine the tensor dataand second sub-data in a to-be-synchronized state among the tensor dataaccording to the descriptor of the tensor data; and

a second determining sub-module configured to determine the firstsub-data according to the second sub-data and the amount of data thatcan be synchronized.

In some embodiments, the data synchronization apparatus furtherincludes:

a state changing module configured to change a state of the firstsub-data of the tensor data from a to-be synchronized state to asynchronized state.

In some embodiments, the descriptor synchronization request instructionincludes an identifier of the descriptor. The descriptor and the amountof data determining module includes:

a first parsing sub-module configured to parse the descriptorsynchronization request instruction to obtain the identifier of thedescriptor and the amount of data that can be synchronized; and

a first descriptor determining sub-module configured to determine thedescriptor of the tensor data to be synchronized according to theidentifier of the descriptor.

In some embodiments, the descriptor synchronization request instructionincludes the data characteristics of the tensor data to be synchronized.The descriptor and the amount of data determining module includes:

a second parsing sub-module configured to parse the descriptorsynchronization request instruction to obtain the data characteristicsof the tensor data to be synchronized and the amount of data that can besynchronized; and

a second descriptor determining sub-module configured to determine thedescriptor of the tensor data according to the data characteristics ofthe tensor data.

FIG. 3e 3 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure. The datasynchronization apparatus is applied to a second processor. As shown inFIG. 3e 3, the data synchronization apparatus includes:

a request instruction generating module 61 e configured to generate adescriptor synchronization request instruction according to a descriptorof tensor data to be synchronized and the amount of data that can besynchronized among the tensor data, where the descriptor is used toindicate a shape of the tensor data to be synchronized, the descriptorsynchronization request instruction is used to instruct a firstprocessor to determine the descriptor of the tensor data to besynchronized and first sub-data of the tensor data according to thedescriptor synchronization request instruction, and the amount of thefirst sub-data corresponds to the amount of data that can besynchronized; and

a request instruction sending module 62 e configured to send thedescriptor synchronization request instruction to the first processor.

In some embodiments, the data synchronization apparatus furtherincludes:

a descriptor and sub-data determining module configured to, when adescriptor synchronization instruction is received from a firstprocessor, determine a descriptor of tensor data to be synchronized andfirst sub-data of the tensor data; and

a data storing module configured to store the first sub-data of thetensor data according to the descriptor of the tensor data.

It should be understood that the apparatus embodiment described above isonly schematic, and the apparatus provided in the present disclosure maybe implemented in other manners. For example, division of theunits/modules is only logical function division and another divisionmanner may be adopted during practical implementation. For example, aplurality of units or components may be combined or integrated intoanother system or some characteristics may be neglected or notperformed.

In addition, unless otherwise specified, each functional unit/module inthe embodiments of the disclosure may be integrated into a unit/module,each unit/module may also physically exist independently, and two ormore units/modules may also be integrated into one unit/module. Theintegrated unit/module may be implemented in the form of hardware or asoftware functional unit/module.

If the integrated unit/module is implemented in the form of hardware,the hardware may be a digital circuit, an analogue circuit, and thelike. The physical implementation of hardware may include, but is notlimited to, a transistor, a memristor, and the like. Unless otherwisespecified, the artificial intelligence processor may be any appropriatehardware processor, such as CPU, GPU, FPGA, DSP, ASIC, and the like.Unless otherwise specified, the storage unit may be any proper magneticstorage medium or magneto-optic storage medium, for example, an RRAM(Resistive Random Access Memory), a DRAM (Dynamic Random Access Memory),an SRAM (Static Random-Access Memory), an EDRAM (Enhanced Dynamic RandomAccess Memory), an HBM (High-Bandwidth Memory), an HMC (Hybrid MemoryCube), and the like.

If being implemented in the form of a software program module and soldor used as an independent product, the integrated unit/module may bestored in a computer-readable memory. Based on such an understanding,all or part of the technical solutions may be embodied in form ofsoftware product. The computer software product is stored in a memory,including a plurality of instructions configured to enable a computerdevice (which may be a PC, a server, a network device, or the like) toperform all or part of the operations of the method in each embodimentof the application. The memory may include various media capable ofstoring program codes such as a U disk, a mobile hard disk, a read-onlymemory (ROM), a random access memory (RAM), a magnetic disk, or anoptical disk.

In some embodiments, the present disclosure provides an artificialintelligence chip including the above-mentioned data synchronizationapparatus.

In some embodiments, the present disclosure provides a board cardincluding a storage device, an interface apparatus, a control device,and the above-mentioned artificial intelligence chip. The artificialintelligence chip is connected to the storage device, the controldevice, and the interface apparatus, respectively; the storage device isconfigured to store data; the interface apparatus is configured toimplement data transfer between the artificial intelligence chip and anexternal equipment; and the control device is configured to monitor astate of the artificial intelligence chip.

In the above-mentioned embodiments, the description of each embodimenthas its own focus. For parts that are not described in detail in anembodiment, please refer to related descriptions of other embodiments.The technical features of the above-mentioned embodiments may becombined arbitrarily. In order to make the description concise, not allpossible combinations of the various technical features in theabove-mentioned embodiments are described. However, as long as there isno contradiction in the combinations of these technical features, theyshould be regarded as the scope of this specification.

The foregoing may be better understood according to the followingarticles:

A1. A data synchronization method applied to a first processor,comprising:

when a descriptor synchronization request instruction is received from asecond processor, determining a descriptor of tensor data to besynchronized and an amount of data that can be synchronized among thetensor data, wherein the descriptor is used to indicate a shape of thetensor data to be synchronized;

determining first sub-data of the tensor data according to thedescriptor of the tensor data and the amount of data that can besynchronized, wherein the amount of the first sub-data corresponds tothe amount of data that can be synchronized; and

according to the first sub-data, generating a descriptor synchronizationinstruction and sending the descriptor synchronization instruction tothe second processor to instruct the second processor to obtain thefirst sub-data.

A2. The data synchronization method of A1, wherein the determining firstsub-data of the tensor data according to the descriptor of the tensordata and the amount of data that can be synchronized includes:

determining the tensor data and second sub-data in a to-be-synchronizedstate among the tensor data according to the descriptor of the tensordata; and

determining the first sub-data according to the second sub-data and theamount of data that can be synchronized.

A3. The data synchronization method of A1 or A2, further comprising:

changing a state of the first sub-data of the tensor data from a to-besynchronized state to a synchronized state.

A4. The data synchronization method of any one of A1-A3, wherein thedescriptor synchronization request instruction includes an identifier ofthe descriptor, wherein the determining a descriptor of tensor data tobe synchronized and the amount of data that can be synchronized amongthe tensor data when a descriptor synchronization request instruction isreceived from a second processor includes:

parsing the descriptor synchronization request instruction to obtain theidentifier of the descriptor and the amount of data that can besynchronized; and

determining the descriptor of the tensor data to be synchronizedaccording to the identifier of the descriptor.

A5. The data synchronization method of any one of A1-A3, wherein thedescriptor synchronization request instruction includes datacharacteristics of the tensor data to be synchronized, wherein thedetermining a descriptor of tensor data to be synchronized and theamount of data that can be synchronized among the tensor data when adescriptor synchronization request instruction is received from a secondprocessor includes:

parsing the descriptor synchronization request instruction to obtain thedata characteristics of the tensor data to be synchronized and theamount of data that can be synchronized; and

determining the descriptor of the tensor data according to the datacharacteristics of the tensor data.

A6. A data synchronization method applied to a second processor,comprising:

generating a descriptor synchronization request instruction according toa descriptor of tensor data to be synchronized and the amount of datathat can be synchronized among the tensor data, wherein the descriptoris used to indicate a shape of the tensor data to be synchronized, thedescriptor synchronization request instruction is used to instruct afirst processor to determine the descriptor of the tensor data to besynchronized and first sub-data of the tensor data according to thedescriptor synchronization request instruction, and the amount of thefirst sub-data corresponds to the amount of data that can besynchronized; and

sending the descriptor synchronization request instruction to the firstprocessor.

A7. The data synchronization method of A6, further comprising:

when a descriptor synchronization instruction is received from a firstprocessor, determining a descriptor of tensor data to be synchronizedand first sub-data of the tensor data; and

storing the first sub-data of the tensor data according to thedescriptor of the tensor data.

A8. A data synchronization apparatus applied to a first processor,comprising:

a descriptor and data amount determining module configured to, when adescriptor synchronization request instruction is received from a secondprocessor, determine a descriptor of tensor data to be synchronized andan amount of data that can be synchronized among the tensor data,wherein the descriptor is used to indicate a shape of the tensor data tobe synchronized;

a sub-data determining module configured to, determine first sub-data ofthe tensor data according to the descriptor of the tensor data and theamount of data that can be synchronized, wherein the amount of the firstsub-data corresponds to the amount of data that can be synchronized; and

a synchronization instruction generating and sending module configuredto, according to the first sub-data, generate a descriptorsynchronization instruction and send the descriptor synchronizationinstruction to the second processor to instruct the second processor toobtain the first sub-data.

A9. The data synchronization apparatus of A8, wherein the sub-datadetermining module includes:

a first determining sub-module configured to determine the tensor dataand second sub-data in a to-be-synchronized state among the tensor dataaccording to the descriptor of the tensor data; and

a second determining sub-module configured to determine the firstsub-data according to the second sub-data and the amount of data thatcan be synchronized.

A10. The data synchronization apparatus of A8 or A9, further comprising:

a state changing module configured to change a state of the firstsub-data of the tensor data from a to-be synchronized state to asynchronized state.

A11. The data synchronization apparatus of any one of A8-A10, whereinthe descriptor synchronization request instruction includes anidentifier of the descriptor, wherein the descriptor and the amount ofdata determining module includes:

a first parsing sub-module configured to parse the descriptorsynchronization request instruction to obtain the identifier of thedescriptor and the amount of data that can be synchronized; and

a first descriptor determining sub-module configured to determine thedescriptor of the tensor data to be synchronized according to theidentifier of the descriptor.

A12. The data synchronization apparatus of any one of A8-A10, whereinthe descriptor synchronization request instruction includes datacharacteristics of the tensor data to be synchronized, wherein thedescriptor and the amount of data determining module includes:

a second parsing sub-module configured to parse the descriptorsynchronization request instruction to obtain the data characteristicsof the tensor data to be synchronized and the amount of data that can besynchronized; and

a second descriptor determining sub-module configured to determine thedescriptor of the tensor data according to the data characteristics ofthe tensor data.

A13. A data synchronization apparatus applied to a second processor,comprising:

a request instruction generating module configured to generate adescriptor synchronization request instruction according to a descriptorof tensor data to be synchronized and the amount of data that can besynchronized among the tensor data, wherein the descriptor is used toindicate a shape of the tensor data to be synchronized, the descriptorsynchronization request instruction is used to instruct a firstprocessor to determine the descriptor of the tensor data to besynchronized and first sub-data of the tensor data according to thedescriptor synchronization request instruction, and the amount of thefirst sub-data corresponds to the amount of data that can besynchronized; and

a request instruction sending module configured to send the descriptorsynchronization request instruction to the first processor.

A14. The data synchronization apparatus of A13, further comprising:

a descriptor and sub-data determining module configured to, when adescriptor synchronization instruction is received from the firstprocessor, determine the descriptor of the tensor data to besynchronized and the first sub-data of the tensor data; and

a data storing module configured to store the first sub-data of thetensor data according to the descriptor of the tensor data.

A15. An artificial intelligence chip, comprising the datasynchronization apparatus of any one of A8-A14.

A16. An electronic device, comprising the artificial intelligence chipof A15.

A17. A board card, comprising a storage device, an interface apparatus,a control device, and the artificial intelligence chip of A15, wherein

the artificial intelligence chip is connected to the storage device, thecontrol device, and the interface apparatus, respectively;

the storage device is configured to store data;

the interface apparatus is configured to implement data transfer betweenthe artificial intelligence chip and an external equipment; and

the control device is configured to monitor a state of the artificialintelligence chip.

A18. The board card of A17, wherein

the storage device includes a plurality of groups of storage units,wherein each group of the storage units is connected with the artificialintelligence chip by a bus, and the storage units are DDR SDRAMs;

the chip includes a DDR controller configured to control data transferand data storage of each storage unit; and

the interface apparatus is a standard PCIE interface.

With the continuous development of the A1 (Artificial Intelligence)technology, it has gradually obtained wide application and worked wellin the fields of image recognition, speech recognition, and naturallanguage processing, and the like. However, as the complexity of A1algorithms is growing, the amount of data and data dimensions that needto be processed are increasing, therefore, multi-core and/or multi-chipdata are usually required for data processing. When data is synchronizedbetween cores or chips, a synchronization method adopting therelated-art may result in large synchronization overhead and lowprocessing efficiency.

In some embodiments, the present disclosure provides a datasynchronization method. FIG. 1h shows a flowchart of a datasynchronization method according to an embodiment of the presentdisclosure. As shown in FIG. 1h , the data synchronization method isapplied to a first processor (any processor in a processing system), andthe method includes:

a step S11 h: when a synchronization request instruction is receivedfrom a second processor, parsing the synchronization request instructionto obtain data characteristics of tensor data to be synchronized and anamount of data that can be synchronized among the tensor data;

a step S12 h: determining a descriptor of the tensor data to besynchronized according to the data characteristics, where the descriptoris used to indicate a shape of the tensor data to be synchronized;

a step S13 h: determining first sub-data of the tensor data according tothe descriptor of the tensor data and the amount of data that can besynchronized, where the amount of the first sub-data corresponds to theamount of data that can be synchronized; and

a step S14 h: according to the first sub-data, generating asynchronization instruction and sending the synchronization instructionto the second processor to instruct the second processor to obtain thefirst sub-data.

For example, the data to be synchronized may include N-dimensionaltensor data (N is an integer greater than or equal to 0, for example,N=1, 2, or 3).

In some embodiments, during data processing, data synchronizationbetween a plurality of processors (such as a plurality of cores of anartificial intelligence chip) may be executed, for example, an operationresult of a processor A1 may be synchronized to a processor A2 as inputdata of another operation. In this case, a data synchronizationmechanism based on the descriptor can be used to achieve datasynchronization.

In some embodiments, since a non-shared storage space of each processorallocated to the tensor data to be synchronized may be limited, thetensor data cannot be synchronized at the same time. In this case, partof tensor data can be synchronized firstly, and repeated many timesuntil all of the tensor data are synchronized.

In some embodiments, the receiver of data synchronization can issue asynchronization request for part of tensor data, in other words, thereceiver sends a descriptor synchronization request instruction, wherethe descriptor synchronization request instruction may determine thedata characteristics of the tensor data to be synchronized and theamount of data that can be synchronized among the tensor data, i.e., theamount of data that can be contained in the non-shared storage space ofthe receiver allocated to the tensor data.

In some embodiments, the first processor among a plurality of processorsmay be set as the sender of data to be synchronized, and the secondprocessor may be set as the receiver of data synchronization. Both thefirst processor and the second processor are any of the plurality ofprocessors, and the second processor may be of the same type ordifferent from the first processor. The present disclosure does notlimit the type of the first processor and the type of the secondprocessor.

In some embodiments, in the step S11 h, when receiving thesynchronization request instruction from the second processor, the firstprocessor may parse the instruction to obtain content of theinstruction, i.e., the data characteristics of the tensor data to besynchronized and the amount of data that can be synchronized, where thedata characteristics may include at least one of the identifier (forexample, a serial number of data), shape, source, and storage address ofthe tensor data.

In some embodiments, the data characteristics of the tensor data to besynchronized may include information such as the shape, source, andaddress of the tensor data. For example, the tensor data may be from aK-th sender (a K-th processor), the tensor data may be from an operationresult of a convolution operation numbered 200, the address of thetensor data may be a specific address area (for example, an addressADDR0-ADDR127), and the shape of the tensor data may be a specifiedshape (for example, the tensor data may be a 20*10 two-dimensionaltensor). Those skilled in the art can set the data characteristics ofthe tensor data to be synchronized according to the actual situation,which is not limited in the present disclosure.

In some embodiments, in the step S12 h, the first processor maydetermine the descriptor of the tensor data to be synchronized accordingto the data characteristics; in the step S13 h, the first processor maydetermine the tensor data to be synchronized according to thedescriptor, and then determine the part of data that can be synchronizedthis time among the tensor data according to the amount of data that canbe synchronized, i.e., the first sub-data. The amount of the firstsub-data may correspond to the amount of data that can be synchronized,for example, the amount of the first sub-data may be less than or equalto the amount of data that can be synchronized.

In some embodiments, if all the data of the tensor data has not beensynchronized, data that can be synchronized may be selected from thetensor data as the first sub-data. If part of the tensor data has notbeen synchronized, and the amount of data that has not been synchronizedis greater than the amount of data that can be synchronized, data thatcan be synchronized may be selected from the data that has not beensynchronized (second sub-data of the tensor data) as the first sub-data;if the amount of data that has not been synchronized is less than orequal to the amount of data that can be synchronized, the data that hasnot been synchronized can be directly taken as the first sub-data. Itshould be understood that those skilled in the art can determine thefirst sub-data according to the actual situation, which is not limitedin the present disclosure.

In some embodiments, the synchronization request instruction may alsoinclude a range of part of tensor data to be synchronized, such as arange of a storage address of the part of sub-data, so as to determinethe part of data to be synchronized. The first processor may directlydetermine the first sub-data to be synchronized according to the rangeof the part of data.

In some embodiments, in the step S14 h, the first processor may generatea synchronization instruction according to the first sub-data and sendthe synchronization instruction to the second processor. Thesynchronization instruction may include the data characteristics of thetensor data to be synchronized and the first sub-data. After receivingthe synchronization instruction, the second processor may parse thesynchronization instruction to determine the data characteristics andthe first sub-data, determine the descriptor according to the datacharacteristics, and determine the tensor data to be synchronizedaccording to the descriptor, and then store the first sub-data of thetensor data in its own non-shared storage space.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the receiver may issue a synchronization requestinstruction to actively request the synchronization of part of the data,and the sender may determine the sub-data synchronized this timeaccording to the amount of data that can be synchronized received fromthe receiver. According to the sub-data, the synchronization instructionmay be generated and sent, so that the receiver can obtain the sub-datasynchronized this time, thereby reducing the synchronization overheadwithout changing the instruction structure and improving the efficiencyof data synchronization.

In some embodiments, the step S13 h may include:

determining the tensor data and second sub-data in a to-be-synchronizedstate among the tensor data according to the descriptor of the tensordata; and

determining the first sub-data according to the second sub-data and theamount of data that can be synchronized.

For example, the state of data among the tensor data may be set, wherethe data that has been synchronized may be set to a synchronized state,and the data that has not been synchronized may be set to ato-be-synchronized state. In this case, when receiving thesynchronization request instruction from the second processor, the firstprocessor may determine the second sub-data in the to-be-synchronizedstate according to the state of the data among the tensor data; andaccording to the second sub-data and the amount of data that can besynchronized indicated by the synchronization state instruction, thefirst processor may determine the first sub-data to be synchronized thistime.

In some embodiments, if the amount of the second sub-data is greaterthan the amount of data that can be synchronized, the first sub-datasynchronized this time can be selected from the second sub-data; if theamount of the second sub-data is less than or equal to the amount ofdata that can be synchronized, the second sub-data can be directly takenas the first sub-data.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, part of tensor data synchronized this time canbe determined, and then the part of tensor data can be synchronized,thereby improving the efficiency of data synchronization.

In some embodiments, the data synchronization method further includes:changing a state of the first sub-data of the tensor data from a to-besynchronized state to a synchronized state.

For example, after the first processor generates and sends thesynchronization instruction according to the first sub-data of thetensor data, so that the second processor can synchronize the firstsub-data of the tensor data, the first processor may change a state ofdata among the tensor data. In other words, the first processor canchange the state of the first sub-data from the to-be synchronized stateto the synchronized state. In this way, when receiving thesynchronization request instruction from the second processor next time,the first processor can determine data to be synchronized next time frompart of data in the to-be-synchronized state, thereby avoiding repeateddata synchronization and improving data synchronization efficiency.

FIG. 3f 1 shows a flowchart of a data synchronization method accordingto an embodiment of the present disclosure. As shown in FIG. 3f 1, thedata synchronization method is applied to a second processor, and themethod includes:

a step S31 f: according to a descriptor of tensor data to besynchronized, determining data characteristics of the tensor data and anamount of data that can be synchronized among the tensor data, where thedescriptor is used to indicate a shape of the tensor data to besynchronized; and

a step S32 f: according to the data characteristics of the tensor dataand the amount of data that can be synchronized, generating asynchronization request instruction and sending the synchronizationrequest instruction to the first processor, where the synchronizationrequest instruction is used to instruct the first processor to determinethe tensor data to be synchronized and first sub-data of the tensor dataaccording to the synchronization request instruction, and the amount ofthe first sub-data corresponds to the amount of data that can besynchronized.

For example, a receiver of data to be synchronized (i.e., the secondprocessor) may issue a synchronization request for part of the tensordata. When there is tensor data to be synchronized in the secondprocessor, the descriptor of the tensor data can be determined. Thedescriptor may be a registered (created) descriptor indicating the shapeof the tensor data, or a new descriptor registered (created) accordingto the shape parameter of the tensor data, which is not limited in thepresent disclosure.

In some embodiments, in the step S31 f, the second processor maydetermine the data characteristics of the tensor data according to thedescriptor, where the data characteristics may include at least one ofthe identifier (for example, a serial number of data), shape, source,and storage address of the tensor data. In addition, the secondprocessor can determine the amount of data that can be contained in thenon-shared storage space of the second processor allocated to the tensordata, i.e., the amount of data can be synchronized.

In some embodiments, in the step S32 f, according to the datacharacteristics of the tensor data and the amount of data that can besynchronized, the second processor may generate a synchronizationrequest instruction and send the instruction, where the synchronizationrequest instruction is used to instruct the first processor to determinethe tensor data to be synchronized and the first sub-data of the tensordata.

In some embodiments, when receiving the synchronization requestinstruction, a receiver of data to be synchronized (i.e., the firstprocessor) may parse the instruction to determine the datacharacteristics of the tensor data to be synchronized and the amount ofdata that can be synchronized; according to the data characteristics,the receiver may determine the descriptor of the tensor data to besynchronized; according to the descriptor, the receiver may determinethe tensor data to be synchronized, and determine the part of data thatcan be synchronized this time among the tensor data according to theamount of data that can be synchronized, i.e., the first sub-data. Theamount of the first sub-data may correspond to the amount of data thatcan be synchronized, for example, the amount of the first sub-data maybe less than or equal to the amount of data that can be synchronized.

In some embodiments, if all the data of the tensor data has not beensynchronized, data that can be synchronized may be selected from thetensor data as the first sub-data. If part of the tensor data has notbeen synchronized, and the amount of data that has not been synchronizedis greater than the amount of data that can be synchronized, data thatcan be synchronized may be selected from the data that has not beensynchronized (second sub-data of the tensor data) as the first sub-data;if the amount of data that has not been synchronized is less than orequal to the amount of data that can be synchronized, the data that hasnot been synchronized can be directly taken as the first sub-data. Itshould be understood that those skilled in the art can determine thefirst sub-data according to the actual situation, which is not limitedin the present disclosure.

In some embodiments, the synchronization request instruction may alsoinclude a range of part of tensor data to be synchronized, such ascontent of the descriptor of the part of sub-data and a range of astorage address of the part of sub-data, so as to determine the part ofdata to be synchronized.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the receiver may issue a synchronization requestfor part of the tensor data, so that the sender can determine thesub-data synchronized this time, thereby improving the efficiency ofdata synchronization.

In some embodiments, the data synchronization method further includes:

when a synchronization instruction is received from a first processor,parsing the synchronization instruction to obtain data characteristicsof tensor data to be synchronized and first sub-data of the tensor data;

determining the descriptor of the tensor data according to the datacharacteristics; and

storing the first sub-data of the tensor data according to thedescriptor of the tensor data.

For example, the first processor may generate and send thesynchronization instruction according to the data characteristics of thetensor data and the first sub-data. When receiving the synchronizationinstruction, the second processor may parse the instruction to determinethe data characteristics of the tensor data to be synchronized and thefirst sub-data of the tensor data synchronized this time; according tothe data characteristics, the second processor may determine thedescriptor, and then determine the tensor data to be synchronizedaccording to the descriptor; and the second processor may store thefirst sub-data of the tensor data in its own non-shared storage space.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, according to the synchronization instruction,the receiver can determine the descriptor and obtain the sub-datasynchronized this time, thereby reducing the synchronization overhead,improving the efficiency of data synchronization, and achievinginstruction compatibility during instruction transfer and processing.

In some embodiments, there is tensor data to be synchronized in thesender of data to be synchronized, for example, when an operation iscompleted and a result of the operation (tensor data) is obtained, thesender can query the state of the receiver to determine the amount ofdata that can be contained in the non-shared storage space of thereceiver allocated to the tensor data, so that part of tensor data canbe synchronized.

In some embodiments, the present disclosure further provides a datasynchronization method applied to a first processor, including:

according to a descriptor of tensor data to be synchronized, determiningdata characteristics of the tensor data, where the descriptor is used toindicate a shape of the tensor data to be synchronized; and

according to the data characteristics of the tensor data, generating astate query instruction and sending the state query instruction to asecond processor, where the state query instruction is used to instructthe second processor to determine the amount of tensor data to besynchronized and generate a synchronization state instruction.

For example, when the first processor determines that there is tensordata to be synchronized, the first processor may obtain the descriptorof the tensor data. The descriptor may be a registered (created)descriptor indicating the shape of the tensor data, or a new descriptorregistered (created) according to the shape parameter of the tensordata, which is not limited in the present disclosure.

In some embodiments, according to the descriptor of the tensor data, thefirst processor may determine the data characteristics of the tensordata. The data characteristics may include at least one of theidentifier (for example, a serial number of data), shape, source, andstorage address of the tensor data.

In some embodiments, the data characteristics of the tensor data to besynchronized may include information such as the shape, source, andaddress of the tensor data. For example, the tensor data may be from aK-th sender (a K-th processor), the tensor data may be from an operationresult of a convolution operation numbered 200, the address of thetensor data may be a specific address area (for example, an addressADDR0-ADDR127), and the shape of the tensor data may be a specifiedshape (for example, the tensor data may be a 20*10 two-dimensionaltensor). Those skilled in the art can set the data characteristics ofthe tensor data to be synchronized according to the actual situation,which is not limited in the present disclosure.

In some embodiments, according to the data characteristics of the tensordata, the first processor may generate a state query instruction andsend the state query instruction to the second processor. If the secondprocessor already has information (for example, a descriptor indicatingthe tensor data to be synchronized has been registered) of the tensordata, the state query instruction may only include part of the datacharacteristics, such as the identifier of the tensor data, and then thestate query instruction may instruct the second processor to determinethe descriptor of the tensor data to be synchronized according to theidentifier of the tensor data; if the second processor does not haveinformation of the tensor data, the synchronization instruction caninclude more data characteristics, such as the identifier and storageaddress of the tensor data, and then the synchronization instruction mayinstruct the second processor to determine the descriptor of the tensordata to be synchronized. The present disclosure does not limit thespecific content included in the state query instruction.

In some embodiments, if the state query instruction includes theidentifier of the tensor data, the second processor may determine thetensor data to be synchronized according to the identifier, and registeror obtain the descriptor indicating the tensor data to be synchronized.If the state query instruction includes more data characteristics (theidentifier and storage address, etc.), the second processor can registerthe descriptor indicating the tensor data to be synchronized accordingto the data characteristics in the instruction.

In some embodiments, after determining the descriptor of the tensor datato be synchronized, the second processor may determine the space thatcan be allocated to the tensor data corresponding to the descriptor, anddetermine the amount of data that can be synchronized among the tensordata. According to the amount of data that can be synchronized and thedata characteristics, the second processor can generate and send asynchronization state instruction, so that the first processor candetermine the tensor data to be synchronized and the amount of data thatcan be synchronized this time.

According to the above-mentioned data synchronization method provided bythe present disclosure, by setting the descriptor indicating the shapeof the tensor data, the sender can determine the data characteristics ofthe tensor data according to the descriptor, and generate and send thestate query instruction according to the data characteristics toinstruct the receiver to feedback its own state (i.e., the amount ofdata that can be synchronized) according to the state query instruction,therefore, part of tensor data can be synchronized, the synchronizationoverhead can be reduced without changing the instruction structure, andthe efficiency of data synchronization can be improved.

In some embodiments, the data synchronization method further includes:

when the synchronization state instruction is received from the secondprocessor, parsing the synchronization state instruction to determinethe data characteristics of the tensor data to be synchronized and theamount of data that can be synchronized;

determining the descriptor of the tensor data to be synchronizedaccording to the data characteristics;

determining first sub-data of the tensor data according to thedescriptor and the amount of data that can be synchronized, where theamount of the first sub-data corresponds to the amount of data that canbe synchronized; and

according to the first sub-data, generating a synchronizationinstruction and sending the synchronization instruction to the secondprocessor to instruct the second processor to obtain the first sub-data.

For example, when receiving the synchronization state instruction fromthe second processor, the first processor may parse the instruction toobtain content of the instruction, i.e., the data characteristics of thetensor data to be synchronized and the amount of data that can besynchronized. According to the data characteristics, the descriptor ofthe tensor data to be synchronized can be determined, and then thetensor data to be synchronized can be determined; and the part of datathat can be synchronized this time (the first sub-data) is determinedfrom the tensor data according to the amount of data that can besynchronized. The amount of the first sub-data may correspond to theamount of data that can be synchronized, for example, the amount of thefirst sub-data may be less than or equal to the amount of data that canbe synchronized.

In some embodiments, if all the data of the tensor data has not beensynchronized, data that can be synchronized may be selected from thetensor data as the first sub-data. If part of the tensor data has notbeen synchronized, and the amount of data that has not been synchronizedis greater than the amount of data that can be synchronized, data thatcan be synchronized may be selected from the data that has not beensynchronized (second sub-data of the tensor data) as the first sub-data;if the amount of data that has not been synchronized is less than orequal to the amount of data that can be synchronized, the data that hasnot been synchronized can be directly taken as the first sub-data. Itshould be understood that those skilled in the art can determine thefirst sub-data according to the actual situation, which is not limitedin the present disclosure.

In some embodiments, the synchronization state instruction may alsoinclude a range of part of tensor data to be synchronized, such as arange of a storage address of the part of sub-data, so as to determinethe part of data to be synchronized. The first processor may directlydetermine the first sub-data to be synchronized according to the rangeof the part of data.

In some embodiments, the first processor may generate a synchronizationinstruction according to the first sub-data and send the synchronizationinstruction to the second processor. The synchronization instruction mayinclude the data characteristics of the tensor data to be synchronizedand the first sub-data. After receiving the synchronization instruction,the second processor may parse the synchronization instruction todetermine the data characteristics of the tensor data to be synchronizedand the first sub-data of the tensor data, determine the descriptoraccording to the data characteristics, and store the first sub-data ofthe tensor data in its own non-shared storage space.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the descriptor of the tensor data and the amountof data that can be synchronized can be determined according to thesynchronization state instruction from the sender; the sub-datasynchronized this time can be determined according to the amount of datathat can be synchronized; and the synchronization instruction can begenerated and sent according to the sub-data, so that the receiver canobtain the sub-data synchronized this time, thereby reducing thesynchronization overhead and improving the efficiency of datasynchronization.

In some embodiments, the step of determining the first sub-data of thetensor data according to the descriptor and the amount of data that canbe synchronized includes:

according to the descriptor, determining the tensor data to besynchronized and second sub-data in a to-be-synchronized state among thetensor data; and

determining the first sub-data according to the second sub-data and theamount of data that can be synchronized.

For example, the state of data among the tensor data may be set, wherethe data that has been synchronized may be set to a synchronized state,and the data that has not been synchronized may be set to ato-be-synchronized state. In this case, when receiving thesynchronization state instruction from the second processor, the firstprocessor may determine the second sub-data in the to-be-synchronizedstate according to the state of the data among the tensor data; andaccording to the second sub-data and the amount of data that can besynchronized indicated by the synchronization state instruction, thefirst processor may determine the first sub-data to be synchronized thistime.

In some embodiments, if the amount of the second sub-data is greaterthan the amount of data that can be synchronized, the first sub-datasynchronized this time can be selected from the second sub-data; if theamount of the second sub-data is less than or equal to the amount ofdata that can be synchronized, the second sub-data can be directly takenas the first sub-data.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, part of tensor data synchronized this time canbe determined, and then the part of tensor data can be synchronized,thereby improving the efficiency of data synchronization.

In some embodiments, the data synchronization method further includes:changing a state of the first sub-data of the tensor data from a to-besynchronized state to a synchronized state.

For example, after the first processor generates the synchronizationinstruction according to the first sub-data of the tensor data and sendsthe descriptor synchronization instruction to the second processor tomake the second processor synchronize the first sub-data of the tensordata, the first processor can change the state of data among the tensordata. In other words, the first processor can change the state of thefirst sub-data from the to-be synchronized state to the synchronizedstate. In this way, when the state of the second processor is queriednext time and the synchronization state instruction of the secondprocessor is received, data to be synchronized next time can bedetermined from part of data in the to-be-synchronized state, therebyavoiding repeated data synchronization and improving datasynchronization efficiency.

In some embodiments, the present disclosure further provides a datasynchronization method applied to a second processor, and the methodincludes:

when a state query instruction is received from a first processor,parsing the state query instruction to obtain data characteristics oftensor data to be synchronized;

determining a descriptor of the tensor data to be synchronized accordingto the data characteristics, where the descriptor is used to indicate ashape of the tensor data to be synchronized;

determining the amount of data that can be synchronized among the tensordata according to the descriptor of the tensor data; and

according to the data characteristics of the tensor data and the amountof data that can be synchronized, generating a synchronization stateinstruction and sending the synchronization state instruction to thefirst processor, where the synchronization state instruction is used toinstruct the first processor to determine first sub-data of the tensordata, and the amount of the first sub-data corresponds to the amount ofdata that can be synchronized.

For example, when there is tensor data to be synchronized in a sender ofdata to be synchronized, the sender may query the state of the receiver.The first processor (sender) may generate and send the state queryinstruction, and when the second processor receives the state queryinstruction, the second processor may parse the state query instructionto determine the data characteristics of the tensor data to besynchronized, where the data characteristics may include at least one ofthe identifier (for example, a serial number of data), shape, source,and storage address of the tensor data.

In some embodiments, the second processor may determine the descriptorof the tensor data to be synchronized according to the datacharacteristics. The descriptor may be a registered (created) descriptorindicating the shape of the tensor data, or a new descriptor registered(created) according to the shape parameter of the tensor data, which isnot limited in the present disclosure.

In some embodiments, the second processor may determine the tensor datato be synchronized according to the descriptor, and determine the amountof data that can be contained in the non-shared storage space of thesecond processor allocated to the tensor data, i.e., the amount of datathat can be synchronized, so that part of tensor data can besynchronized.

In some embodiments, the second processor may generate and send asynchronization state instruction to the first processor according tothe determined amount of data that can be synchronized and the datacharacteristics of the tensor data, so as to instruct the firstprocessor to determine the amount of data that can be synchronized thistime. After determining the part of data (i.e., the first sub-data) thatcan be synchronized this time, the first processor may generate asynchronization instruction and send the synchronization instruction tothe second processor. The synchronization instruction may include thedata characteristics of the tensor data to be synchronized and the firstsub-data of the tensor data.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the sender may query the state of the receiver;after receiving the state query instruction, the receiver determines andresponds to its own state (i.e., the amount of data that can besynchronized). In this way, part of tensor data can be synchronizedthrough interaction, which may improve the efficiency of datasynchronization.

In some embodiments, the data synchronization method further includes:

when a synchronization instruction is received from the first processor,parsing the synchronization instruction to obtain the datacharacteristics of the tensor data to be synchronized and the firstsub-data of the tensor data;

determining the descriptor of the tensor data according to the datacharacteristics; and

storing the first sub-data of the tensor data according to thedescriptor of the tensor data.

For example, when receiving the synchronization instruction, the secondprocessor may parse the instruction to determine the datacharacteristics of the tensor data to be synchronized and the firstsub-data of the tensor data to be synchronized this time; according tothe data characteristics, the second processor may determine thedescriptor of the tensor data to be synchronized; and according to thedescriptor, the second processor may determine the tensor data to besynchronized, and then store the first sub-data of the tensor data inits own non-shared storage space.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, the receiver can determine the descriptoraccording to the synchronization instruction and obtain sub-datasynchronized this time, thereby reducing synchronization overhead andimproving the efficiency of data synchronization.

In some embodiments, the identifier and content of the descriptor can bestored in the descriptor storage space, where the descriptor storagespace can be a storage space in an internal memory (such as a register,an on-chip SRAM, or other medium cache, etc.) of the processor. The datastorage space of the tensor data indicated by the descriptor may be astorage space in the internal memory (such as an on-chip cache) of theprocessor or a storage space in an external memory (an off-chip memory)connected to the processor. The data address in the data storage spacemay be an actual physical address or a virtual address. The presentdisclosure does not limit a position of the descriptor storage space anda position of the data storage space, and the type of the data address.

In some embodiments, the identifier and content of the descriptor, andtensor data indicated by the descriptor can be stored in a same area.For example, a continuous area of an on-chip cache with addressesADDR0-ADDR1023 can be used to store the above information. Within thisarea, addresses ADDR0-ADDR31 can be used to store the identifier of thedescriptor, addresses ADDR32-ADDR63 can be used to store the content ofthe descriptor, and addresses ADDR64-ADDR1023 can be used to store thetensor data indicated by the descriptor. The address ADDR is not limitedto 1 bit or 1 byte, and is an address unit used to represent an address.Those skilled in the art can determine the storage area and the addressthereof according to the specific applications, which is not limited inthe present disclosure.

In some embodiments, the identifier and content of the descriptor, andthe tensor data indicated by the descriptor can be respectively storedin different areas of an internal memory. For example, a register can beused as a descriptor storage space to store the identifier and contentof the descriptor, and an on-chip cache can be used as a data storagespace to store the tensor data indicated by the descriptor.

In some embodiments, a special register (SR) may be provided for thedescriptor, where data in the descriptor may be an immediate number orbe obtained from the special register. When the register is used tostore the identifier and content of the descriptor, a serial number ofthe register can be used to indicate the identifier of the descriptor.For example, if the serial number of the register is 0, the identifierof a descriptor stored in the register is 0. When the descriptor in theregister is valid, an area can be allocated in a caching space (such ascreating a tensor caching unit for each piece of tensor data in thecache) according to a size of tensor data indicated by the descriptorfor storing the tensor data. It should be understood that a presetcaching space may also be used to store the tensor data, which is notlimited in the present disclosure.

In some embodiments, the identifier and content of the descriptor can bestored in an internal memory, and the tensor data indicated by thedescriptor can be stored in an external memory. For example, theidentifier and content of the descriptor may be stored on-chip and thetensor data indicated by the descriptor may be stored off-chip.

In some embodiments, the data address of the data storage spacecorresponding to the descriptor may be a fixed address. For example, aseparate data storage space may be allocated for the tensor data, and astart address of each piece of tensor data in the data storage spacecorresponds to an identifier of the descriptor. In this case, theprocessor can determine the data address of the tensor data according tothe content of the descriptor.

In some embodiments, when the data address of the data storage spacecorresponding to the identifier of the descriptor is a variable address,the descriptor may be also used to indicate the address of N-dimensionaltensor data, where the content of the descriptor may further include atleast one address parameter representing the address of the tensor data.For example, if the tensor data is a 3-dimensional data, when thedescriptor points to the address of the tensor data, the content of thedescriptor may include an address parameter indicating the address ofthe tensor data, such as a start address of the tensor data; or thecontent of the descriptor may include a plurality of address parametersof the address of the tensor data, such as a start address+addressoffset of the tensor data, or address parameters of the tensor data ineach dimension. Those skilled in the art can set the address parametersaccording to actual needs, which is not limited in the presentdisclosure.

In some embodiments, the address parameter of the tensor data includes abase address of the datum point of the descriptor in the data storagespace of the tensor data, where the base address may be differentaccording to the change of the datum point. The present disclosure doesnot limit the selection of the datum point.

In some embodiments, the base address may include a start address of thedata storage space. When the datum point of the descriptor is a firstdata block of the data storage space, the base address of the descriptoris the start address of the data storage space. When the datum point ofthe descriptor is other data than the first data block in the datastorage space, the base address of the descriptor is the physicaladdress of the data block in the data storage space.

In some embodiments, the shape parameter of the tensor data includes atleast one of the followings: a size of the data storage space of thetensor data in at least one of N dimensions, a size of the storage areain at least one of the N dimensions, an offset of the storage area in atleast one of the N dimensions, a position of at least two vertices atdiagonal positions in the N dimensions relative to the datum point, anda mapping relationship between a data description position of the tensordata indicated by the descriptor and the data address of the tensor dataindicated by the descriptor. The data description position is a mappingposition of a point or an area in the tensor data indicated by thedescriptor, for example, if the tensor data is 3-dimensional data, thedescriptor can use a coordinate (x, y, z) to represent the shape of thetensor data, and the data description position of the tensor data can berepresented by the coordinate (x, y, z), and the data descriptionposition of the tensor data may be a position of a point or an areawhere the tensor data is mapped in a 3-dimensional space.

It should be understood that those skilled in the art may select a shapeparameter representing tensor data according to actual conditions, whichis not limited in the present disclosure.

By adopting the above-mentioned data synchronization method provided bythe present disclosure, when the space of the receiver of datasynchronization is insufficient, part of tensor data can be synchronizedfirstly, and repeated many times until all of the tensor data aresynchronized, which can avoid the problems of overall synchronizationfailure or synchronization delay of tensor data in the case ofinsufficient space, and improve the efficiency of data synchronization.In addition, the descriptor indicating the shape of the tensor data isset, and the tensor data is determined according to the descriptorduring the data synchronization process, thereby reducingsynchronization overhead and reducing the complexity of data access, andachieving the instruction compatibility during transfer and processingprocess.

It should be noted that, for the sake of simple description, the abovemethod embodiments are all described as a series of action combinations.However, those skilled in the art should be aware that the presentdisclosure is not limited by the described action order, becauseaccording to the present disclosure, certain steps may be executed inanother order or executed simultaneously. Those skilled in the artshould also be aware that the embodiments described in the specificationare alternative embodiments and that the actions and modules involvedare not necessary in the present disclosure.

It should be further noted that although the steps in the flow chartsare shown in sequence as indicated by the arrows, these steps are notnecessarily executed in the order indicated by the arrows. Unlessspecifically stated in the present disclosure, the execution of thesesteps is not strictly limited in order, and these steps may be executedin other orders. In addition, at least part of the steps in in the flowcharts may include a plurality of sub-steps or stages. These sub-stepsor stages are not necessarily executed at the same time, but may beexecuted at different times. The execution of these sub-steps or stagesis not necessarily performed sequentially, but may be performedalternately with other steps or at least a part of the sub-steps orstages of other steps.

FIG. 3f 2 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure. The datasynchronization apparatus is applied to a first processor. As shown inFIG. 3f 2, the data synchronization apparatus includes:

a request instruction parsing module 51 f configured to, when asynchronization request instruction is received from a second processor,parse the synchronization request instruction to obtain datacharacteristics of tensor data to be synchronized and the amount datathat can be synchronized among the tensor data;

a first descriptor determining module 52 f configured to determine adescriptor of the tensor data to be synchronized according to the datacharacteristics, where the descriptor is used to indicate a shape of thetensor data to be synchronized;

a data determining module 53 f configured to determine first sub-data ofthe tensor data according to the descriptor of the tensor data and theamount of data that can be synchronized, where the amount of the firstsub-data corresponds to the amount of data that can be synchronized; and

a synchronization instruction generating and sending module 54 fconfigured to, according to the first sub-data, generate asynchronization instruction and sending the synchronization instructionto the second processor to instruct the second processor to obtain thefirst sub-data.

In some embodiments, the data determining module includes:

a first determining sub-module configured to determine the tensor dataand second sub-data in a to-be-synchronized state among the tensor dataaccording to the descriptor of the tensor data; and

a second determining sub-module configured to determine the firstsub-data according to the second sub-data and the amount of data thatcan be synchronized.

In some embodiments, the data synchronization apparatus furtherincludes:

a state changing module configured to change a state of the firstsub-data of the tensor data from a to-be synchronized state to asynchronized state.

FIG. 3f 3 shows a block diagram of a data synchronization apparatusaccording to an embodiment of the present disclosure. The datasynchronization apparatus is applied to a second processor. As shown inFIG. 3f 3, the data synchronization apparatus includes:

a characteristics and data amount determining module 61 f configured to,according to a descriptor of tensor data to be synchronized, determinedata characteristics of the tensor data and the amount of data that canbe synchronized among the tensor data, where the descriptor is used toindicate a shape of the tensor data to be synchronized; and

a request instruction generating and sending module 62 f configured to,according to the data characteristics of the tensor data and the amountof data that can be synchronized, generate a synchronization requestinstruction and sending the synchronization request instruction to thefirst processor, where the synchronization request instruction is usedto instruct the first processor to determine the tensor data to besynchronized and first sub-data of the tensor data according to thesynchronization request instruction, and the amount of the firstsub-data corresponds to the amount of data that can be synchronized.

In some embodiments, the data synchronization apparatus furtherincludes:

a synchronization instruction parsing module configured to, when asynchronization instruction is received from the first processor, parsethe synchronization instruction to obtain the data characteristics ofthe tensor data to be synchronized and the first sub-data of the tensordata;

a second descriptor determining module configured to determine thedescriptor of the tensor data according to the data characteristics; and

a data storing module configured to store the first sub-data of thetensor data according to the descriptor of the tensor data.

It should be understood that the apparatus embodiment described above isonly schematic, and the apparatus provided in the present disclosure maybe implemented in other manners. For example, division of theunits/modules is only logical function division and another divisionmanner may be adopted during practical implementation. For example, aplurality of units or components may be combined or integrated intoanother system or some characteristics may be neglected or notperformed.

In addition, unless otherwise specified, each functional unit/module inthe embodiments of the disclosure may be integrated into a unit/module,each unit/module may also physically exist independently, and two ormore units/modules may also be integrated into one unit/module. Theintegrated unit/module may be implemented in the form of hardware or asoftware functional unit/module.

If the integrated unit/module is implemented in the form of hardware,the hardware may be a digital circuit, an analogue circuit, and thelike. The physical implementation of hardware may include, but is notlimited to, a transistor, a memristor, and the like. Unless otherwisespecified, the artificial intelligence processor may be any appropriatehardware processor, such as CPU, GPU, FPGA, DSP, ASIC, and the like.Unless otherwise specified, the storage unit may be any proper magneticstorage medium or magneto-optic storage medium, for example, an RRAM(Resistive Random Access Memory), a DRAM (Dynamic Random Access Memory),an SRAM (Static Random-Access Memory), an EDRAM (Enhanced Dynamic RandomAccess Memory), an HBM (High-Bandwidth Memory), an HMC (Hybrid MemoryCube), and the like.

If being implemented in the form of a software program module and soldor used as an independent product, the integrated unit/module may bestored in a computer-readable memory. Based on such an understanding,all or part of the technical solutions may be embodied in form ofsoftware product. The computer software product is stored in a memory,including a plurality of instructions configured to enable a computerdevice (which may be a PC, a server, a network device, or the like) toperform all or part of the operations of the method in each embodimentof the application. The memory may include various media capable ofstoring program codes such as a U disk, a mobile hard disk, a read-onlymemory (ROM), a random access memory (RAM), a magnetic disk, or anoptical disk.

In some embodiments, the present disclosure provides an artificialintelligence chip including the above-mentioned data synchronizationapparatus.

In some embodiments, the present disclosure provides a board cardincluding a storage device, an interface apparatus, a control device,and the above-mentioned artificial intelligence chip. The artificialintelligence chip is connected to the storage device, the controldevice, and the interface apparatus, respectively; the storage device isconfigured to store data; the interface apparatus is configured toimplement data transfer between the artificial intelligence chip and anexternal equipment; and the control device is configured to monitor astate of the artificial intelligence chip.

In the above-mentioned embodiments, the description of each embodimenthas its own focus. For parts that are not described in detail in anembodiment, please refer to related descriptions of other embodiments.The technical features of the above-mentioned embodiments may becombined arbitrarily. In order to make the description concise, not allpossible combinations of the various technical features in theabove-mentioned embodiments are described. However, as long as there isno contradiction in the combinations of these technical features, theyshould be regarded as the scope of this specification.

The foregoing may be better understood according to the followingarticles:

A1. A data synchronization method applied to a first processor,comprising:

when a synchronization request instruction is received from a secondprocessor, parsing the synchronization request instruction to obtaindata characteristics of tensor data to be synchronized and an amount ofdata that can be synchronized among the tensor data;

determining a descriptor of the tensor data to be synchronized accordingto the data characteristics, wherein the descriptor is used to indicatea shape of the tensor data to be synchronized;

determining first sub-data of the tensor data according to thedescriptor of the tensor data and the amount of data that can besynchronized, wherein the amount of the first sub-data corresponds tothe amount of data that can be synchronized; and

according to the first sub-data, generating a synchronizationinstruction and sending the synchronization instruction to the secondprocessor to instruct the second processor to obtain the first sub-data.

A2. The data synchronization method of A1, wherein the determining firstsub-data of the tensor data according to the descriptor of the tensordata and the amount of data that can be synchronized includes:

determining the tensor data and second sub-data in a to-be-synchronizedstate among the tensor data according to the descriptor of the tensordata; and

determining the first sub-data according to the second sub-data and theamount of data that can be synchronized.

A3. The data synchronization method of A1 or A2, further comprising:

changing a state of the first sub-data of the tensor data from a to-besynchronized state to a synchronized state.

A4. A data synchronization method applied to a second processor,comprising:

according to a descriptor of tensor data to be synchronized, determiningdata characteristics of the tensor data and the amount of data that canbe synchronized among the tensor data, wherein the descriptor is used toindicate a shape of the tensor data to be synchronized; and

according to the data characteristics of the tensor data and the amountof data that can be synchronized, generating a synchronization requestinstruction and sending the synchronization request instruction to afirst processor, wherein the synchronization request instruction is usedto instruct the first processor to determine the tensor data to besynchronized and first sub-data of the tensor data according to thesynchronization request instruction, and the amount of the firstsub-data corresponds to the amount of data that can be synchronized.

A5. The data synchronization method of A4, further comprising:

when a synchronization instruction is received from the first processor,parsing the synchronization instruction to obtain the datacharacteristics of the tensor data to be synchronized and the firstsub-data of the tensor data;

determining the descriptor of the tensor data according to the datacharacteristics; and

storing the first sub-data of the tensor data according to thedescriptor of the tensor data.

A6. A data synchronization apparatus applied to a first processor,comprising:

a request instruction parsing module configured to, when asynchronization request instruction is received from a second processor,parse the synchronization request instruction to obtain datacharacteristics of tensor data to be synchronized and an amount of datathat can be synchronized among the tensor data;

a first descriptor determining module configured to determine adescriptor of the tensor data to be synchronized according to the datacharacteristics, wherein the descriptor is used to indicate a shape ofthe tensor data to be synchronized;

a data determining module configured to determine first sub-data of thetensor data according to the descriptor of the tensor data and theamount of data that can be synchronized, wherein the amount of the firstsub-data corresponds to the amount of data that can be synchronized; and

a synchronization instruction generating and sending module configuredto, according to the first sub-data, generate a synchronizationinstruction and send the synchronization instruction to the secondprocessor to instruct the second processor to obtain the first sub-data.

A7. The data synchronization apparatus of A6, wherein the datadetermining module includes:

a first determining sub-module configured to determine the tensor dataand second sub-data in a to-be-synchronized state among the tensor dataaccording to the descriptor of the tensor data; and

a second determining sub-module configured to determine the firstsub-data according to the second sub-data and the amount of data thatcan be synchronized.

A8. The data synchronization apparatus of A6 or A7, further comprising:

a state changing module configured to change a state of the firstsub-data of the tensor data from a to-be synchronized state to asynchronized state.

A9. A data synchronization apparatus applied to a second processor,comprising:

a characteristics and data amount determining module configured to,according to a descriptor of tensor data to be synchronized, determinedata characteristics of the tensor data and the amount of data that canbe synchronized among the tensor data, wherein the descriptor is used toindicate a shape of the tensor data to be synchronized; and

a request instruction generating and sending module configured to,according to the data characteristics of the tensor data and the amountof data that can be synchronized, generate a synchronization requestinstruction and send the synchronization request instruction to a firstprocessor, wherein the synchronization request instruction is used toinstruct the first processor to determine the tensor data to besynchronized and first sub-data of the tensor data according to thesynchronization request instruction, and the amount of the firstsub-data corresponds to the amount of data that can be synchronized.

A10. The data synchronization apparatus of A9, further comprising:

a synchronization instruction parsing module configured to, when asynchronization instruction is received from the first processor, parsethe synchronization instruction to obtain the data characteristics ofthe tensor data to be synchronized and the first sub-data of the tensordata;

a second descriptor determining module configured to determine thedescriptor of the tensor data according to the data characteristics; and

a data storing module configured to store the first sub-data of thetensor data according to the descriptor of the tensor data.

A11. An artificial intelligence chip, comprising the datasynchronization apparatus of any one of A6-A10.

A12. An electronic device, comprising the artificial intelligence chipof A11.

A13. A board card comprising a storage device, an interface apparatus, acontrol device, and the artificial intelligence chip of A11, wherein theartificial intelligence chip is connected to the storage device, thecontrol device, and the interface apparatus, respectively;

the storage device is configured to store data;

the interface apparatus is configured to implement data transfer betweenthe artificial intelligence chip and an external equipment; and

the control device is configured to monitor a state of the artificialintelligence chip.

A14. The board card of A13, wherein the storage device includes aplurality of groups of storage units, wherein each group of the storageunits is connected with the artificial intelligence chip by a bus, andthe storage units are DDR SDRAMs:

the chip includes a DDR controller configured to control data transferand data storage of each storage unit; and

the interface apparatus is a standard PCIE interface.

The embodiments of the present disclosure have been described above, andthe above description is exemplary, not exhaustive, and is not limitedto the disclosed embodiments. Without departing from the scope andspirit of the described embodiments, many modifications and changes areobvious to those ordinary skilled in the art. The terms used herein areintended to better explain the principles, practical applications, orimprovements to technologies in the market of the embodiments, or toenable other ordinary skilled in the art to understand the embodimentsdisclosed herein.

1-20. (canceled)
 21. A data processing method, comprising: determiningthat a decoded processing instruction is a descriptor managementinstruction for managing a descriptor used to indicate a shape of atensor data; obtaining a management parameter of the descriptor in theprocessing instruction, wherein the management parameter includes atleast one of an identifier of the descriptor, the shape of the tensordata indicated by the descriptor, or content of the tensor dataindicated by the descriptor; and executing the processing instructionaccording to the management parameter.
 22. The data processing method ofclaim 21, wherein the descriptor management instruction is a descriptorregistration instruction, wherein the executing the processinginstruction according to the management parameter includes: registeringthe descriptor according to at least one of the identifier of thedescriptor, the shape of the tensor data indicated by the descriptor, orthe content of the tensor data indicated by the descriptor.
 23. The dataprocessing method of claim 22, wherein the registering the descriptoraccording to at least one of the identifier of the descriptor, the shapeof the tensor data indicated by the descriptor, and the content of thetensor data indicated by the descriptor further comprises: determining afirst storage area for content of the descriptor in a descriptor storagespace, and a second storage area for the tensor data in a data storagespace; determining the content of the descriptor to indicate the secondstorage area according to at least one of the shape of the tensor dataindicated by the descriptor, or the content of the tensor data indicatedby the descriptor, and storing the content of the descriptor in thefirst storage area.
 24. The data processing method of claim 23, whereinthe management parameter includes the shape of the tensor data indicatedby the descriptor, and the content of the descriptor is determinedaccording to a correspondence between the shape of the tensor data and adata address of the second storage area.
 25. The data processing methodof claim 23, wherein the management parameter includes the identifier ofthe descriptor, wherein registering the descriptor according to at leastone of the identifier of the descriptor, the shape of the tensor dataindicated by the descriptor, and the content of the tensor dataindicated by the descriptor further comprises: when the identifierindicates the first storage space, determining the first storage spaceaccording to the identifier; or when the identifier does not indicatethe first storage space, establishing a correspondence between theidentifier of the descriptor and the first storage space.
 26. The dataprocessing method of claim 21, wherein the content of the tensor dataindicated by the descriptor includes at least one of immediate data ordata in a register.
 27. The data processing method of claim 21, whereinthe descriptor management instruction is a descriptor releaseinstruction, and the management parameter includes the identifier of thedescriptor, wherein the executing the processing instruction accordingto the management parameter further comprises: releasing the descriptorcorresponding to the identifier according to the identifier of thedescriptor.
 28. The data processing method of claim 27, wherein thereleasing the descriptor corresponding to the identifier according tothe identifier of the descriptor further comprises: releasing a storagearea of the descriptor in a descriptor storage space and a storage areaof the content of the tensor data indicated by the descriptor in a datastorage space, respectively.
 29. The data processing method of claim 27,further comprising: storing the content of the descriptor in adesignated storage space according to the identifier of the descriptor.30. A data processing apparatus, comprising a control circuit configuredto: determine that a decoded processing instruction is a descriptormanagement instruction for managing a descriptor used to indicate ashape of a tensor data; obtain a management parameter of a descriptor inthe processing instruction, wherein the management parameter includes atleast one of an identifier of the descriptor, the shape of the tensordata indicated by the descriptor, or content of the tensor dataindicated by the descriptor; and execute the processing instructionaccording to the management parameter.
 31. The data processing apparatusof claim 30, wherein the descriptor management instruction is adescriptor registration instruction, wherein to execute the processinginstruction according to the management parameter, the control circuitis further configured to: determine a first storage area for content ofthe descriptor in a descriptor storage space, and a second storage areafor the tensor data in a data storage space; determine the content ofthe descriptor to indicate the second storage area according to at leastone of the shape of the tensor data indicated by the descriptor, or thecontent of the tensor data indicated by the descriptor, and store thecontent of the descriptor in the first storage area.
 32. The dataprocessing apparatus of claim 31, wherein the management parameterincludes the identifier of the descriptor, wherein the control circuitis further configured to: when the identifier indicates the firststorage space, determine the first storage space according to theidentifier; or when the identifier does not indicate the first storagespace, establish a correspondence between the identifier of thedescriptor and the first storage space.
 33. The data processingapparatus of claim 30, wherein the descriptor management instruction isa descriptor release instruction, and the management parameter includesthe identifier of the descriptor, wherein to execute the processinginstruction according to the management parameter, the control circuitis further configured to: release the descriptor corresponding to theidentifier according to the identifier of the descriptor.
 34. The dataprocessing apparatus of claim 33, wherein to release the descriptorcorresponding to the identifier according to the identifier of thedescriptor, the control circuit is further configured to: release astorage area of the descriptor in a descriptor storage space and astorage area of the content of the tensor data indicated by thedescriptor in a data storage space, respectively.
 35. An artificialintelligence chip, comprising the data processing apparatus of claim 30.36. The artificial intelligence chip of claim 35, wherein the descriptormanagement instruction is a descriptor registration instruction, whereinto execute the processing instruction according to the managementparameter, the control circuit is further configured to: determine afirst storage area for content of the descriptor in a descriptor storagespace, and a second storage area for the tensor data in a data storagespace; determine the content of the descriptor to indicate the secondstorage area according to at least one of the shape of the tensor dataindicated by the descriptor, or the content of the tensor data indicatedby the descriptor, and store the content of the descriptor in the firststorage area.
 37. The artificial intelligence chip of claim 36, whereinthe management parameter includes the identifier of the descriptor,wherein the control circuit is further configured to: when theidentifier indicates the first storage space, determine the firststorage space according to the identifier; or when the identifier doesnot indicate the first storage space, establish a correspondence betweenthe identifier of the descriptor and the first storage space.
 38. Theartificial intelligence chip of claim 35, wherein the descriptormanagement instruction is a descriptor release instruction, and themanagement parameter includes the identifier of the descriptor, whereinto execute the processing instruction according to the managementparameter, the control circuit is further configured to: release astorage area of the descriptor in a descriptor storage space and astorage area of the content of the tensor data indicated by thedescriptor in a data storage space, respectively, according to theidentifier of the descriptor.
 39. An electronic device, comprising theartificial intelligence chip of claim
 35. 40. A board card, comprising astorage device, an interface apparatus, a control device, and theartificial intelligence chip of claim 35, wherein the artificialintelligence chip is connected to the storage device, the controldevice, and the interface apparatus, respectively; the storage device isconfigured to store data; the interface apparatus is configured toimplement data transfer between the artificial intelligence chip and anexternal equipment; and the control device is configured to monitor astate of the artificial intelligence chip.